Strategic basin and delta planning increases the resilience of the Mekong Delta under future uncertainty

Abstract

Significance
Globally, river deltas, which support some of the planet’s most productive agroeconomic systems and half a billion livelihoods, are at risk of being drowned by rising sea levels and accelerated subsidence. Whether delta land falls below sea level will depend on land and water management in the delta, sediment supply from the upstream basin, and global climate change. Those drivers cover multiple scales and domains and are rapidly changing, uncertain, and interconnected, which makes finding robust strategies to increase the resilience of river deltas challenging. Herein, we demonstrate an approach to identify planning levers that can increase the resilience of river deltas under a wide range of future conditions for the 40,000-km ² Mekong Delta in Southeast Asia.

Full text

Strategic basin and delta planning increases the
resilience of the Mekong Delta under
future uncertainty
R. J. P. Schmitt
a,b,1
, M. Giuliani
c
, S. Bizzi
c,d
, G. M. Kondolf
e
, G. C. Daily
a,b,1
, and Andrea Castelletti
c,f

a
Natural Capital Project, Stanford University, Stanford, CA 94305;
b
The Woods Institute for the Environment, Stanford University, Stanford, CA 94305;
c
Department of Electronics, Information, and Bioengineering, Politecnico di Milano, 20133 Milano, Italy;
d
Department of Geosciences, University of Padova,
35122 Padova, Italy;
e
Department of Landscape Architecture and Environmental Planning, University of California, Berkeley, CA 94720; and
f
Institute of
Environmental Engineering, ETH Zurich, 8049 Zurich, Switzerland
Contributed by G. C. Daily, July 26, 2021 (sent for review December 18, 2020; reviewed by Frances E. Dunn and Daniel Peter Loucks)
The climate resilience of river deltas is threatened by rising sea
levels, accelerated land subsidence, and reduced sediment supply
from contributing river basins. Yet, these uncertain and rapidly
changing threats are rarely considered in conjunction. Here we
provide an integrated assessment, on basin and delta scales, to
identify key planning levers for increasing the climate resilience of
the Mekong Delta. We find, first, that 23 to 90% of this unusually
productive delta might fall below sea level by 2100, with the large
uncertainty driven mainly by future management of groundwater
pumping and associated land subsidence. Second, maintaining
sediment supply from the basin is crucial under all scenarios for
maintaining delta land and enhancing the climate resilience of the
system. We then use a bottom-up approach to identify basin de-
velopment scenarios that are compatible with maintaining sedi-
ment supply at current levels. This analysis highlights, third, that
strategic placement of hydropower dams will be more important
for maintaining sediment supply than either projected increases in
sediment yields or improved sediment management at individual
dams. Our results demonstrate 1) the need for integrated planning
across basin and delta scales, 2) the role of river sediment man-
agement as a nature-based solution to increase delta resilience,
and 3) global benefits from strategic basin management to main-
tain resilient deltas, especially under uncertain and changing
conditions.
Mekong
|
river deltas
|
resilience
|
sediment
|
global environmental change
By 2050, 1 billion people will live in low-lying coastal zones
and river deltas (1), areas that are also of paramount im-
portance for regional and global food security (2–4). The ma-
jority of the world’s most productive and densely populated
coastal areas, such as the deltas of the Mekong (5–7), Irrawaddy
(8), Ganges-Brahmaputra (9), Nile (10), and Mississippi (11), are
experiencing significant subsidence and land loss as a result of
reduced sediment supply from upstream basins, unsustainable
management of water and sediment in the deltas, and global sea-
level rise (12, 13). The subsidence of these major deltas relative
to rising sea levels foreshadows fundamental failures of critical
food systems and livelihoods (1, 2, 12–14).
Hard engineering approaches maintain some heavily subsided
deltas (4) but come at the cost of major capital investments and
lock-ins into nonsustainable conditions (3, 15). An alternative is
to maintain or increase the natural resilience of river deltas,
i.e., their ability to recover from shocks and adapt to change
through natural processes (3). The resilience of river deltas de-
pends on a balance between processes that accelerate subsidence
of the delta surface, sea-level rise, and land accretion driven by
sediment supply from upstream rivers (2, 5, 13, 16, 17). Anthro-
pogenic change is altering most of these processes, and impacts
will increase in the future (1, 2, 12, 17, 18). For example, global
sea levels are rising (1), unsustainable groundwater extraction is
widespread (10, 19, 20), and basin development is reducing sedi-
ment supply to many deltas (5, 16, 17, 21, 22).
As basin- and delta-scale processes are connected, an inte-
grated perspective would be key to identifying strategies for
improving the resilience of delta land and livelihoods. Basin and
delta scales are still mostly disconnected, however, in both sci-
entific studies and management plans. While some delta-scale
studies have attempted to translate reduced sediment supply into
measures of sea-level rise relative to the subaerial delta surface
(relative sea-level rise, rSLR) (5, 16, 21), they were limited to few
scenarios or qualitative assessments to estimate future sediment
supply from contributing river basins. Moreover, they neither
explored multiple possible futures of basin development nor
accounted for the uncertain and competing drivers of sediment
generation and transport in large river basins. More granular
studies of basin development have, in turn, produced quantita-
tive estimates of changing sediment supply from (sub)basin to
global scales but were not coupled to models of delta morphol-
ogy, key to translating changing sediment supplies into endpoint
measures of rSLR and delta resilience (17, 18, 23–26).
Importantly, previous studies did not evaluate uncertainty in
future sediment supply arising from competing and highly uncer-
tain drivers. Available evidence suggests that sediment trapping in
Significance
Globally, river deltas, which support some of the planet’s most
productive agroeconomic systems and half a billion liveli-
hoods, are at risk of being drowned by rising sea levels and
accelerated subsidence. Whether delta land falls below sea
level will depend on land and water management in the delta,
sediment supply from the upstream basin, and global climate
change. Those drivers cover multiple scales and domains and
are rapidly changing, uncertain, and interconnected, which
makes finding robust strategies to increase the resilience of
river deltas challenging. Herein, we demonstrate an approach
to identify planning levers that can increase the resilience of
river deltas under a wide range of future conditions for the
40,000-km
2
Mekong Delta in Southeast Asia.
Author contributio ns: R.J.P.S., M.G., S. B., G.M.K., G.C.D., and A .C. designed research ;
R.J.P.S., M.G., and S.B. performed research; R.J.P.S. and M.G. contributed new reagents/
analytic tools; R.J.P.S. and M.G. analyzed data; and R.J.P.S., M.G., S.B., G.M.K., G.C.D., and
A.C. wrote the paper.
Reviewers: F.E.D., Utrecht University; and D.P.L., Cornell University.
The authors declare no competing interest.
This open access article is distributed under Creative Commons Attribution-NonCommercial-
NoDeriv atives L icense 4.0 ( CC BY-NC- ND).
1
To whom correspondence may be addressed. Email: rschmitt@stanford.edu or gdaily@
stanford.edu.
This article contains supporting information online at https://www.pnas.org/lookup/suppl/
doi:10.1073/pnas.2026127118/-/DCSupplemental.
Published September 2, 2021.
PNAS 2021 Vol. 118 No. 36 e2026127118 https://doi.org/10.1073/pnas.2026127118
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past and future dams will outweigh increased sediment yields from
climate and land-use change for most basins (17, 18). While this is
an important finding, it comes from studies focused on full de-
velopment of global hydropower potentials (27). This is a limita-
tion, because decreasing prices of alternative renewables will likely
reduce the demand for hydropower, thereby enabling strategic
hydropower planning with least cumulative impacts (24, 25, 28,
29). Sediment trapping could also be reduced by optimized design
and management of individual dam projects (30, 31). However,
the cumulative benefits of such approaches have not been dem-
onstrated at the scale of large basins, with many planned dams and
under changing environmental conditions.
A framework that integrates basin and delta processes and
accounts for their interactions and uncertainty in their future
magnitude and direction could be used to design robust strate-
gies to minimize rSLR and increase delta resilience. Here, we
present such an integrated framework and demonstrate its ap-
plication to the Mekong Basin and Delta (Fig. 1A). The frame-
work combines a basin and delta component and builds on
concepts of exploratory modeling and decision scaling (32–34).
We use this framework to identify key drivers of rSLR in the
Mekong Delta. Mostly less than 2 m above sea level (6), the
Mekong Delta is globally outstanding in terms of supported
livelihoods and food production (35). The Mekong Delta is also
outstanding in terms of imminent threats across scales, from
unsustainable groundwater use and land management (19, 20,
36–39) in the delta to upstream hydropower development (22,
40) and global climate change (5, 41), thus presenting a chal-
lenging and relevant case study.
Our framework helps to address three main research gaps.
First, we study the sensitivity of rSLR to delta-scale drivers
(DSDs, e.g., sea level rise and accelerated subsidence) and basin-
scale drivers (sediment supply). Second, we analyze different
scenarios of hydropower development in the basin with regard to
their impacts on sediment supply. Using a basin model (25) we
subject each hydropower scenario to many realizations of sedi-
ment yields and sediment trapping in dams. Third, we perform a
bottom-up analysis linking sediment supply to hydropower de-
velopment as well as to other basin-scale drivers, e.g. climate and
land-use change (42, 43). Instead of prescribing a few scenarios
we map the response of sediment supply to a wide range of
basin-scale drivers and identify which combinations of those
drivers are compatible with certain levels of sediment supply (42,
43) and thus certain levels of future resilient delta land.
Results
First, we explore different scenarios of DSDs and sediment
supply (Methods) for the Mekong Delta. We then model for each
of those scenarios how much of the delta surface would remain
subaerial (i.e., above sea level) and how much would fall below
sea level by 2100. For that, we deploy a conceptual model of
delta morphodynamics (Methods), which translates DSDs and
sediment supply into an aggregate measure of rSLR. The results
allow us to explore if rSLR is more sensitive to DSDs or to
sediment supply. Using recent topographic data (6), we also map
which parts of the delta would fall below sea level for different
values of rSLR.
Our estimates of rSLR range from 0.23 m to 1.39 m by 2100.
Because of the extremely low topography of the delta, such rSLR
would result in 7.4 to 89% loss of the subaerial delta land
(i.e., land above sea level) (Fig. 1B). Relative sea level (rSLR) is
influenced by DSDs more than by sediment supply, such that
even with pristine sediment supply for the basin (∼160 Mt/y)
subaerial land would change by −7.4 and −76.2% (Fig. 1B,Top).
However, sediment supply modulates how much delta land re-
mains above sea level within each DSD scenario. Varying sedi-
ment supply between 160 and 5 Mt/y results in −7.4 to −26.9%
(best-case DSD), −37.7 to −63.5% (central-case DSD), and −76.2
to −89.2% (worst-case DSD) of land above sea level (Fig. 1 Band
C). Thus, rSLR is most sensitive to sediment supply for the central
case of DSDs, for which changes in sediment supply from 160 Mt/y
(natural load) down to 5 Mt/y [with sediment trapping by full dam
buildout (22)] produce a 25.8% variation in land above the sea
level (37.7 to 63.5%). The same variation in sediment supply with
the best-case DSD scenario results in a 19.5% variation (−7.4 to
26.9%), and the worst-case DSD scenario results in a 13% vari-
ation (76.2 to 89.2%). For the central-range DSD scenario, sedi-
ment supply makes the difference between large swaths of land
along the southeastern shorelines and in the northeastern center
of the delta falling below or remaining above the sea level.
Stretches of the eastern delta would fall below sea level even
under best-case DSDs (Fig. 1D, green). Worst-case DSDs would
increasingly drown higher-lying areas along the distributary
channels (Fig. 1D, light purple to orange).
Thus, rSLR and land above sea level is mostly controlled by
delta-scale management of accelerated subsidence. However,
sediment supply also plays an important role. Sediment supply
will be reduced by dams (35), but the magnitude of reduction
depends on where and how dams are built and operated (24, 25,
30, 31). Similarly, sand mining in the lower Mekong will reduce
sediment supply (51), but how much depends on locations and
rates of mining [subject to market forces and enforcement of
government regulations (35)]. Also, road construction (44) and
changing land use and climate (42, 43, 45) might alter sediment
yields and thus how much sediment is supplied to rivers. How-
ever, especially for climate drivers the direction of change is
uncertain and might differ throughout the basin (41).
To model joint impacts of changing sediment yields and dam
siting we considered 17 different scenarios of dam siting. Each of
the 17 scenarios represents a different portfolio of dams, i.e., a
different set of dam sites, and is associated with one of 11 levels
of increasing hydropower generation, referred to as generation
levels 1 to 11 (GL1 to GL11). GL6 is the status quo, i.e., GL1 to
GL5 represent the past construction of dams up to the current
generation of 140,000 GWh/y at GL6 (Fig. 2 Aand B, purple).
Starting from the existing dams at GL6, we analyzed two di-
verging trajectories for the future. The first trajectory results
from a business-as-usual future of hydropower development (Fig.
2Aand B, red). As an alternative, we analyzed dam portfolios
optimized for connectivity between the delta and the basin, re-
ferred to as the Mekong Delta connectivity (MDC) sequence (25)
(blue in Fig. 2 Aand B). We then subjected each of the 17
portfolios (one historic portfolio for GL1 to GL5 and two each for
GL6 to GL11) to 130,000 Monte Carlo analysis (MCA) runs of
the CASCADE network sediment model (25). In each run, we
varied parameters for sediment yield from seven geomorphic
provinces (22, 25) and for sediment trapping in the dams of five
riparian countries (25) (Methods).
Our analysis advances studies of sediment trapping in Mekong
dams (22, 25, 40), constraining the possible range of sediment
supply for any dam portfolio (Fig. 2A). We estimate that sedi-
ment delivery to the delta with current dams can be around 58 ±
17 Mt/y (mean ±1 SD over the 130,000 MCA runs) (Fig. 2A,
GL6), with the variability originating from uncertain sediment
supply and uncertain sediment trapping in existing dams. This
reduction in sediment supply to the delta would result in an
estimated −23 to −87% change in subaerial delta land by 2100
(Fig. 2A, GL6, right yaxis).
Current plans for expanding hydropower focus on large dams
in the lower Mekong tributaries and the mainstem of the
Mekong in Laos and Cambodia (Fig. 2B, e.g., GL8, red mark-
ers). This would increase generation to around 194,000 GWh/y
(GL8) but decrease sediment supply to 31 ±13 Mt/y, with the
tail of the distribution reaching 5 Mt/y. For full hydropower
development (GL11) the mean sediment delivery to the delta is
reduced to 9 ±1.5 Mt/y. Such a reduction in sediment supply to
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future uncertainty
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the delta would result in a 27 to 90% change in subaerial delta
land. With more dams in the lower Mekong Basin sediment
supply to the delta becomes less sensitive to changing sediment
yields from hillslope processes and sediment trapping in dams
(Fig. 2, see more and more narrow “violins”for GL8 to GL12).
Thus, for the planned dam sequence even future increases in
sediment yields and decreases in sediment trapping would not
result in more sediment supply to the delta. This is because cu-
mulative sediment trapping in large dams on the lower Mekong
mainstem would outweigh additional sediment from changing
catchment processes and better sediment passage through
upstream dams.
The optimized dam portfolio for GL8 would consist mostly of
dams upstream of existing dams, both in China and on lower
Mekong tributaries (Fig. 2B, GL8, blue markers). With that dam
portfolio, sediment supply could be maintained at or slightly
below the current rate. Beyond GL8, even optimized dam
portfolios will lead to decreasing sediment supply. However, up
to GL10, sediment supply is significantly higher for the MDC
than for the currently planned sequence (compare violin plot for
GL8, GL9, and GL10 for the MDC vs. planned sequence).
Reaching GL8 following the MDC sequence would reduce the
surface of the subaerial delta by −23 to −87%, compared to
around −27 to −90% for the planned sequence (both for the
central estimates of sediment supply).
The proliferation of dams in the basin not only reduces total
sediment supply but also changes the sensitivity of sediment
supply to basin-scale processes. In the past, when there were few
N
China
Burma
Thailand
Viet Nam
India
Cambodia
Laos
1
2
34
5
6
7
Hydropower Dams
Status
Build/
Under construction
Potential
Major rivers
Sediment Yield
[t/km2/yr]
0
40
160
200
250
280
290
450
National Border
Elevation [m]
6852
0
Mekong Delta
0 200 400
km
1Lancang (LCG)
2
Northern
Highlands
(NH)
3Loei Fold Belt (LFB)
4Mun-Chi Basin (MCB)
5
Annamite
Mountains
(AM)
6Kon Tum Massif (KTM)
7
Tertiary Volcanic
Plateau
(TVP)
Best
estimates
Central
estimates
Worst
estimates
Delta Scale Drivers (DSDs)
050100
% land below sea level
by 2100
0.23
0.33
0.41
0.45
0.48
0.62
0.72
0.82
0.87
0.90
1.04
1.15
1.26
1.33
1.39
-7.4 % -37.7 % -76.2 %
-17.3 % -46.3 % -83.6 %
-22.5 % -54.9 % -87.0 %
-24.9 % -60.3 % -88.3 %
-26.9 % -63.5 % -89.2 %
160
100
50
25
5
Sediment Supply [Mt/y]
Relative Sea Level Rise (rSLR) by 2100 [m]
panel d
0.23
0.33
0.41
0.45
0.48
0.62
0.72
0.82
0.87
0.90
1.04
1.15
1.26
1.33
1.39
Below sea level for rSLR of [m]
>2
DSDs: Best estimates
DSDs: Central estimates
DSDs: Worst estimates
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Fig. 1. Up to 90% of the Mekong Delta might fall below sea level by 2100. (A) The 800,000-km
2
Mekong basin can be divided into distinct geomorphic
provinces (22) (numbers 1 to 7), each with a different contribution to the sediment budget of the basin. (B) Different levels of sediment supply from the basin
(rows) together with different scenarios for DSDs (columns) result in different levels of rSLR (colors) and thus different fractions of the current delta surface
remaining subaerial, i.e., above sea level (percentages). (C) The change in subaerial delta surface for each level of rSLR in Bbased on most recent topographic
data (6). (D) These topographic data are used to locate areas below sea level. Refer to SI Appendix, Fig. S2 for trajectories of subaerial delta surface from 2020
to 2100 and for continuous levels (0 to 160 Mt/y) of sediment supply. Note that colors in B,C, and Dare corresponding.
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dams in the basin (GL1, Fig. 2B), sediment supply was most
sensitive to sediment yields in the Lancang (Fig. 3A, GL1), the
upper part of the basin in China (Fig. 1A). As large dams were
built on the lower Lancang (GL2 to GL5, Fig. 2B), sediment
supply to the delta became less sensitive to sediment yields in the
Lancang. Instead, sensitivity to the sediment trapping in dams in
China, i.e., the Lancang, increased (Fig. 4A, sediment trapping
China for GL2 to GL4). Today, sediment supply is no longer sen-
sitive to sediment trapping rates in the Chinese dams either. This is
because dams in the Lancang are many and very large (Fig. 3A,
GL6). Thus, cumulatively dams in the Lancang will trap most in-
coming sediment even if the trapping rates in individual dams in the
Lancang were lower than expected. As the Lancang became more
and more disconnected (Fig. 3A,GL4toGL6),sedimentsupply
became increasingly sensitive to sediment yields in the lower
basin, notably the Tertiary Volcanic Plateau (TVP) in Laos and
Vietnam and the Northern Highland of Laos (Fig. 1A).
If higher generation levels are achieved following the planned
trajectory of hydropower development (Fig. 2B, red circles),
most of the basin will be decoupled from the delta in the near
future. If the planned sequence of dams would be developed up
to GL8 (Fig. 2B, red circles) sediment supply would only be
sensitive to yields in the TVP and to sediment trapping by dams
in Laos and Cambodia (Fig. 3B, GL8). Sediment supply would
remain sensitive to similar drivers for MDC sequence, which in
contrast mostly includes dams upstream of existing dams, for
GL6 up to GL8.
We then map the response of sediment supply to continuous
changes in the most sensitive drivers over the entire analyzed
parameter range (Fig. 3 D–G). We use these response maps in a
bottom-up manner to estimate which combinations of basin-
scale drivers are compatible with certain levels of sediment
supply. Bottom-up studies on engineered systems often rely on
well-defined thresholds for system success or failure (34) and
response maps can identify conditions for either system success
or failure. For a complex human–natural system, such as a river
delta, there are no clear failure/success thresholds. Instead, delta
resilience will continuously scale with sediment supply (Fig. 1B).
Therefore, we herein use the central estimate of current sedi-
ment supply (58 Mt/y) as an illustrative threshold. We then study
under which conditions this threshold would be met or exceeded.
In the future, different thresholds can be derived from partici-
patory processes or transboundary dialogues (32) and analyzed
using this bottom-up approach.
We illustrate the bottom-up approach for GL8 following the
MDC sequence. If sediment yields in the Lancang and TVP
would decrease by 25% (Points A in Fig. 3 Dand F), mean
sediment supply to the delta would decrease to an average of 50
Mt/y (Fig. 3D). The probability of attaining the 58 Mt/y threshold
would be only around 25% (Fig. 3F). If sediment yields in the
Lancang and TVP would increase by 25% (Points C in Fig. 3 D
0 0.15 0.45 0.75 1.04 1.39 1.64 1.94 2.24 2.53 2.70
0
50
100
150
200
250
300
Sediment Supply [Mt/y]
-39
1LG
2
L
G
3LG
4LG
5LG
7LG
8
LG
9LG
01LG
-S
M
ADLL
A
1LG1
Delta-scale
drivers
Best estimates
Central estimates
Worst estimates
-47 -56 -66
-8 -23 -27
Change in subaerial delta surface
[%]
-17
Past
Future
GL 1 GL 2 GL 5 GL 6
Added dams
Past
Planned
MDC future
Rivers
GL 7 GL 8 GL 9 GL 10 GL 11
Present
-77 -84 -87 -90
-YA
D
O
T
1
1LG
Central estimates
MDC sequence
Planned sequence
Distribution:
Sediment supply
MDC
Planned
Past sequence Past
B
A
Fig. 2. Business-as-usual hydropower development will lead to significantly less sediment supply than strategic dam development. (A) Each point indicates
the central estimate for sediment supply and hydropower for a specific dam portfolio. We selected 17 dam portfolios resulting in 11 distinct generation levels
(GL1 to GL11) for a detailed analysis. For each of those portfolios, we estimated uncertainty in sediment supply using 130,000 Monte Carlo runs of a network
sediment model. Violin plots for each portfolio demonstrate the statistical distribution of results. The right yaxis links different levels of sediment supply to a
reduction in the subaerial delta surface (i.e., land above sea level) by 2100. This reduction is shown for three different scenarios of DSDs (compare Fig. 1B). (B)
Spatial layouts for past (purple) and future generation levels shown in A. Note that for the future there are two different scenarios for reaching each
generation level: planned (red circles) and an optimized alternative (blue squares). See also SI Appendix, Fig. S3.
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and F) mean sediment supply to the delta would increase to an
average of 66 Mt/y (Fig. 3D). The probability of attaining the 58
Mt/y threshold would be 60%. Similar reasoning can be applied
to sediment trapping in Laos and Cambodia. If sediment trap-
ping in dams in both countries is 25% less (Point D in Fig. 3 E
and G) the central estimate of sediment supply would increase to
around 64 Mt/yr (Point D in Fig. 3E) and the probability of
attaining 58 Mt/y would be around 55%.
We can further analyze if the MDC sequence robustly results
in a higher probability to attain the 58 Mt/y threshold compared
with the planned sequence even under future uncertainty. With
the planned sequence of dam construction, there is a very low
probability of attaining the 58 Mt/y threshold for most combi-
nations of basin-scale drivers (Fig. 4 Aand B). The only possible
case would be if sediment trapping in dams in Laos were less
than half the central estimates and 58 Mt/y could be attained
with around 50% probability (point A in Fig. 4B). By contrast,
the MDC sequence would attain the 58 Mt/y threshold with the
same probability for central estimates of sediment trapping
(marker CE in Fig. 4 Cand D).
Fig. 4 Eand Fshow the difference in probabilities between the
MDC and the planned sequence. This comparison highlights
conditions of sediment yield and sediment trapping under which
the MDC sequence would perform better than the planned
ABC
DE
FG
Probability of attaining sediment
Fig. 3. Infrastructure decisions alter the impact of basin-scale drivers on sediment supply to the Mekong Delta. Sensitivity of sediment supply to the Mekong
Delta and sediment trapping parameters for the past (A), the planned future (B), and the optimized MDC scenario (C). Sensitivity is measured as Sobol index,
ranging from 0 (no sensitivity) to 1 (high sensitivity) computed over 130,000 MCA realizations for each dam portfolio (Fig. 2B). (D–G) The response of sediment
supply to continuous changes in the most sensitive drivers for GL8 for the MDC sequence in terms of resulting mean sediment supply (D,E) and in terms of
attaining at least the current level of supply (58 Mt/y). Points A and C mark a 25% decrease or increase in sediment yields compared to central estimates (Point
B). Points D and F mark a 25% decrease or increase in sediment trapping compared to central estimates (Point E).
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sequence. Notably, the likelihood of the MDC sequence out-
performing the planned sequence increases with more optimistic
estimates of sediment yield and sediment trapping (yellow and
purple in Fig. 4 Eand F). Importantly, we find no conditions under
which the planned sequence outperforms the MDC sequence.
To constrain potential future changes in sediment yield, we
analyzed projections of sediment yields by 2030 (43) and by 2070
(42) (SI Appendix, Supplemental Methods). Each assessment in-
cluded different climate and land-use projections. Cross and star
markers in Fig. 4 A,C, and Eindicate the projected relative
change in sediment yields from the Lancang and the TVP. While
there is considerable variability in projected sediment yields both
datasets indicate a decrease in sediment yield in the Lancang and
an increase for the TVP for moderate emission scenarios, e.g.,
RCP2.6 (Representative Concentration Pathway 2.6). Projec-
tions for RCP4.5 by Borelli et al. (42) (Fig. 4A, B_4.5) indicate a
major increase in sediment yield from the TVP (+ 70%), while
yields in the Lancang remain mostly stable (+8%). Projections
from the same study for RCP8.5 (Fig. 4A, B_8.5) indicate instead
a major increase in sediment yields from the Lancang (+40%)
and a smaller increase in sediment yields in the TVP (+ 25%).
Chuenchum et al.’s projections for RCP4.5 and RCP8.5 (Fig. 4,
C_4.5 and C_8.5) indicate lower sediment yields from the Lan-
cang (−22 and –11%) and increases in sediment yield from the
TVP (+46 and +51%) (43).
Under B_4.5 and B_8.5, optimized dam portfolios would at-
tain a sediment supply of 58 Mt/y with around 80% (B_4.5) and
65% probability (B_8.5) (Fig. 4B). Compared to our baseline
(Fig. 4E, point CE) most projections (except B_2.6) would in-
crease the probability of attaining the 58 Mt/y threshold. Nota-
bly, the MDC sequence has an even greater benefit for B_4.5 and
B_8.5 than for the baseline conditions. Thus, changing sediment
yields increase the advantage of the MDC sequence over the
planned sequence in terms of attaining the 58 Mt/y threshold
(B_4.5 and B_8.5 in Fig. 4E). Only if much less sediment is
supplied to rivers does this advantage decrease (light blue in
Fig. 4E). Those conditions could occur if extreme weather events
decrease in magnitude and frequency (41).
There are no projections of future sediment trapping in dams.
On the one hand, stricter environmental safeguards might lead
to designing and operating future dams for less sediment trapping
compared our central estimates (31). On the other hand, many
dams in the Mekong were built without large bottom outlets or
other features needed for sediment management, which hinders
future implementation of sediment management in existing dams
(30). If improved sediment management in dams is possible in the
future, the cumulative benefits of those dam-scale measures need
to be leveraged by strategic hydropower planning. The relative
advantage of the MDC sequence (Fig. 4D) over the planned se-
quence (Fig. 4B) increases from 40% (Fig. 4F, CE) to nearly 60%
if sediment trapping in dams is reduced by 25% compared to the
central estimate (Fig. 4F,B).
Discussion
Globally, coastal populations are growing (1, 2), and climate
change (1) and major infrastructure plans (14, 17, 27) threaten
the resilience of many large river deltas. Our results from the
Mekong Delta, which supports more than 17 million livelihoods
and food systems of global importance (35), point to the need for
sustainable land, water, and sediment management on delta and
basin scales. While river basin and delta processes are commonly
disconnected in research and management, our findings high-
light the importance of integrating assessments across scales to
estimate future rSLR and to identify effective management le-
vers for achieving delta resilience in a robust manner.
Our results from integrating basin- and delta-scale models
emphasize that rSLR in the Mekong Delta will be driven by
accelerated subsidence from nonsustainable groundwater abstrac-
tions (5, 19, 20, 36, 38). The area of the delta remaining above
raising sea levels will change by −23 to −87% by 2100, even if
sediment supply stays at or close to current levels (around 58 Mt/y).
Delta planning must consider this imminent threat to agriculture
and other livelihoods and the potential for permanent land loss.
A
B
C
D
E
F
Fig. 4. Strategic planning is essential to leverage lower sediment trapping in dams and higher sediment yields for more sediment supply to the delta. (Aand
B) Probability of attaining current levels of sediment supply (58 Mt/y) with the planned dam sequence. (Cand D) Same as Aand B, but for the strategic MDC
hydropower sequence. (E) Difference between Aand C.(F) Difference between Band D. Cross and asterisk markers indicate projections of sediment yield
from Borelli et al. (42) (+ markers) for the year 2070 and by Chuenchum et al. (43) (* markers) for the year 2030 using different RCPs and land-use scenarios.
Numbers indicate the RCPs, prefixes “C_”and “B_”indicate projections from either Borrelli et al. (42) or Chenchum et al. (43). Square markers A and B are
discussed in the text.
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Despite the dominance of DSDs, maximizing sediment supply is
crucial to maintain at least some delta land above sea level (3, 4,
12), especially if DSDs are managed more sustainably in the future.
Integrated planning is thus needed to evaluate future decisions in
the basin regarding their impacts on delta resilience and comparing
monetary benefits from development activities, e.g., hydropower or
sand mining, to costs of adaptation, relocation, and coastal
engineering in the delta.
Strategic infrastructure planning is paramount for maintaining
sediment supply to the Mekong Delta (22, 24, 25, 40) and thus
for maximizing its resilience. Specifically, further business-as-usual
dam development is not compatible with maintaining sediment
supply even at the current levels, which are strongly reduced
compared to the basin’s pristine state. Only strategic dam planning
across all riparian countries could produce a modest increase in
hydropower generation while maintaining sediment supply close
to current levels. Our analyses highlight that strategic infrastruc-
ture planning on a portfolio level is a robust strategy to minimize
cumulative environmental impacts of dams even under major
uncertainty. This finding has implications beyond the Mekong
because most of the global hydropower potential is in large and
poorly studied basins, where origins of sediment and other natural
values are poorly constrained and subject to change, and where
the design and operation of future dams is highly uncertain (27).
For the Mekong, our results indicate that better design and
management of individual dams is unlikely to translate into better
sediment supply if dams are not developed in a strategic manner.
Of course, environmental safeguards on the scale of individual
dams can alleviate local negative externalities and might seem
more practical from a political perspective than a basin-scale
planning process. However, our results show that only strategic
planning on a portfolio level ensures that local safeguards, such as
better sediment management, result in basin-scale improvements
in environmental objectives.
Our analyses relied on a multistage exploratory process to
constrain most significant drivers of rSLR and to focus the analysis
on drivers with the greatest relative uncertainty. However, future
research could include the entire connected system model,
i.e., including both basin and delta processes, into the bottom-up
analysis. Ideally, future analyses would also improve process rep-
resentation in the delta to capture the spatial heterogeneity in
groundwater extraction and natural compaction (19, 20, 38, 46), as
well as feedbacks between delta management and sediment ac-
cretion (39). Results could then be used by regional stakeholders
to negotiate trade-offs and to develop adaptation plans (47) that
consider realistic levels of future rSLR and acknowledge the in-
trinsic links between processes on delta, basin, and global scales.
Making deltas more resilient will require navigating conflicting
objectives on multiple scales, within overarching uncertainties
and shocks from global climate change and collapse of nature.
Integrating parsimonious models that jointly represent deltas
and their contributing basins can provide critical information to
make delta management robust to future uncertainty and help in
establishing delta resilience as a crucial objective in river basin
management.
Methods
We base our analyses on three main components. First, we developed a
conceptual morphodynamic model of the Mekong Delta, which, in contrast
to previous iterations (5), considers dynamic feedbacks between land loss
and aggradation. This model allows us to transfer basin and DSDs into a
common metric of rSLR. We then use the latest topography of the Mekong
Delta (6) to identify which areas would fall below sea level for different rates
of rSLR (e.g., Fig. 1). Throughout the paper, this model is used to translate
sediment supply rates into estimates delta surface below sea level by the end
of the century.
Second, we use a network-scale sediment routing model (25, 48) to an-
alyze impacts of changing sediment yields and dam sediment trapping on
sediment supply. We use the model to simulate different scenarios of dam
development. For each scenario, we perform an MCA, consisting of 130,000
runs of the basin model. In each run, the model implements a new combi-
nation of sediment yields from seven geomorphic provinces and dam sedi-
ment trapping in five riparian countries.
Finally, we implement a variance-based sensitivity analysis (49, 50) to
determine sensitivity of sediment supply to a total of 12 different parame-
ters for each of the 17 dam portfolios.
Delta Plane Model. The delta plane model (5) is used to estimate future
subaerial land in the Mekong Delta for different combinations of basin and
DSDs. The delta plane model is based on a sediment mass balance for the
delta, which then allows us to put changes in sediment supply (in megatons
per year) and accelerated subsidence (in millimeters per year) into a common
metric of rSLR (in millimeters per year). The model is run with annual
timesteps to 2100.
In each year, subsidence of the delta plane because of local drivers is
determined as
SUBS(t)=CMP(t)+PMP(t),
where SUBS is total subsidence, CMP is natural compaction, and PMP is
subsidence from groundwater pumping (all in millimeters per year) at time t
[years]. Subsidence is counteracted by sediment supply from the basin, which
accretes delta land as it spreads out over the delta surface. The effect of this
land building is expressed as
ACC(t)=SED*(t)
ρS
*1
ADelta(t),
where SED*is the sediment supply (tons per year), ρSis the density of de-
posited material (tons per cubic meter), ADelta is the subaerial extend of the
delta (square meters), and ACC is the resulting accretion. Note that SED*
considers for the effect of sand mining
SED*=max(0, SED(t)−MNG(t)).
Thus, if rates of mining (MNG) exceed sediment supply, there will be no more
supply to the delta, but the rate of supply cannot become negative. In reality
erosion could replace parts of the mined sediment, i.e., increasing sediment
supply at the cost of incision and lateral erosion of river channels and as-
sociated negative externalities (51, 52).
For both mining and pumping, we assume that rates will decrease over
time, both because of stricter environmental regulations and possible land
loss and decreasing agricultural area, so that
PMP(t)=PMP(t=0)*mt
and
MNG(t)=MNG(t=0)*mt,
where m<1 denotes a rate factor [−].
Finally, rates of rSLR in each time step can be determined as
rSLR(t)=SLR(t)+SUBS(t)−ACC(t)
and the cumulative rSLR is then
rSLRtot(t)=∑
k∈{1...t−1}
rSLRtot(k),
which can be compared to recent topographic data (Fig. 1E) (6) and a hyp-
sometric curve of the delta (SI Appendix, Fig. S1) to identify which parts of
the delta fall below sea level, and where those parts are located.
Dynamic versus Static Delta Plane Model. As indicated above, accretion rates
will depend upon the subaerial delta surface on which sediment can accrete.
In a static formulation, we assume that ADelta (t)=const =40,000*106m2.Ina
dynamic formulation, we assume that the area of the subaerial delta is
variable, decreasing with increasing cumulative rSLR. As the supplied sedi-
ment will then spread over less area, the marginal accretion (i.e., in milli-
meters per ton) resulting from sediment supply will increase as the delta
shrinks (5, 21, 53).
To estimate the subaerial delta land, we use the gridded digital elevation
model of the delta provided by Minderhoud et al. (6) (SI Appendix, Fig. S1
shows the hypsometric curve of the delta plane). We calculate delta land
remaining above sea level as
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Strategic basin and delta planning increases the resilience of the Mekong Delta under
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ADelta(t)=∑
h(i)>rSLR tot(t)
i*ci,
where idenotes a cell in the digital elevation model of the delta and ciis the
cell size of that cell (square meters). Thus, we form the sum of all cells that
above the sea level rise for time t.
We derive a value of rSLR for both dynamic and static model formulations
rSLRtot,dyn(t)and rSLRtot,stat (t)by the year 2100 (SI Appendix, Fig. S2). Results
are reported as the mean of the two models in the year 2100. We take this
approach because neither the static nor the dynamic models is more realistic
per se. Rather, we propose that the two models present an extreme enve-
lope for accretion. Thus, rSLR is calculated as
rSLRtot(2100)=rSLRtot ,dyn(2100)+rSLRtot ,stat(2100)
2.
We separately report the outcomes of each model in SI Appendix, Fig. S2.
Similar to ref. 5, we propose three different scenarios for global and local
drivers of subsidence. These drivers are natural subsidence, pumping, sand
mining, and sea level rise. Each of the scenarios combines either a minimum,
maximum, or central estimate value for each driver, all derived from
reviewing pertinent published data (5). The ambition of this analysis is not to
model the detailed morphologic development of the future delta but rather
to provide a backdrop for how different rates of sediment supply, which
couple the basin and the delta models, together with DSDs, will create very
different futures for the delta.
The delta plane model comes with some relevant simplifications. First, we
assume that sediment is spread across the entire delta surface. In a natural delta,
areas closer to rivers and depressions would receive more deposition. In a heavily
modified delta, sediment deposition is further altered by infrastructure for ir-
rigation and flood protection (39). Second, we assume that DSDs are constant in
space. Instead, new research indicates spatial variability, e.g., in natural com-
paction and pumping-related subsidence (19, 20, 36, 37, 46). Third, the model
considers neither the role and dynamics of different grain sizes, e.g., sand from
natural bed-load transport versus finer sediment from hillslope processes, nor
off-shore sediment transport (54). These points call for efforts to build spatially
explicit models for delta management, which could draw on recent spatial
datasets of subsidence (6, 36, 38, 46). Finally, estimates for some drivers might
seem dire but are still optimistic with regard to local management, as we as-
sume decreasing trends of unsustainable groundwater use and sand mining.
While this assumption is in line with mitigation scenarios proposed in other
papers (36), irrigation abstractions are still increasing in parts of the delta (55),
highlighting the need for better water m anagement in the delta.
Sediment Routing and Trapping Model. The functioning of the model used for
this analysis is described elsewhere (25). In a nutshell, the model uses a graph-
based routing scheme to represent transport of sediment from each node in
the river network to the basin outlet. The sediment supply (in tons per year)
at each source node ς[denoted y(ς)] is determined by the local sediment
yield (in tons per square kilometer per year) and the area supplying sedi-
ment to the node (in square kilometers). The local sediment yield is deter-
mined by its location within one of seven geomorphic provinces.
Sediment trapping rates for each dam are determined by applying the
Brune curve (56). According to the nonlinear Brune model, sediment trap
efficiency in a dam, TE(d), is a function of hydraulic residence time in the
associated reservoir, which can be derived for current and future dams from
available regional datasets (25).
Thus, the sediment routing model has a total of 131 parameters. First,
there are 124 estimates of trap efficiency in dams. Then, there are estimates
of sediment yield at each node in the river network, but as sediment yield is
assumed constant in each geomorphic province, yield parameters collapse to
seven values, one for each geomorphic province.
Global Sensitivity Analysis. To test model sensitivity to these parameters, we
applied a Sobol sensitivity analysis, a variance decomposition approach that
attributes variance in the simulated model output to individual input pa-
rameters and their interactions (49, 50). Sampling the full space of all 131
input parameters would have resulted in an infeasibly large number of
model simulations. Thus, we grouped the dams of each country together
and defined a country-specific multiplier for the trap efficiency of dams in
each country c. Using this multiplier, eTE (c), also reflects that environmental
regulations for dam design and operation and requirements for sediment
passage would potentially be enacted on a national level. Thus, for the
sensitivity analysis, sediment trapping in a dam is determined by
TE’(d)=eTE (c)*TE(d),
i.e., the central estimate of sediment trapping derived from dam-specific
parameters and a country-specific multiplier. It should be noted that more
detailed future studies for, e.g., smaller dam portfolios on specific
tributaries, could adopt a similar approach on a single-dam level.
Similarly, the sediment yield at each node is modified by a multiplier that is
specific to the geomorphic province, g. Using this multiplier, ey(g), allows us
to modify the central estimates of sediment yield so that
y’(ς)=ey(g)*y(ς),
where gis the geomorphic province in which source node ςis located.
Scenarios of sediment supply and sediment trapping are then generated
through a two-step scheme to sample the 12-dimensional parameter space
(five countries and seven geomorphic provinces). For that, we used Sobol
quasi-random sampling coupled with Saltelli’s cross-sampling method (57)
aimed at uniformly covering the multidimensional input space of eparam-
eters (49). We set the range of eTE to 0.25 to 1.5 and the range of eyto 0.2 to
2. Note that higher values of eTE represent more sediment trapping and thus
less supply to the delta, while higher values of eywill lead to more sediment
supply to the delta. Note that we capped eTE at a lower value (1.5) than ey
(2.0). This is mostly because we propose that most uncertainty in sediment
trapping is due to the unknown design and operation of run-of-river dams
on the lower Mekong. For those dams, which might be partially drawn down
during the flood season, sediment trapping is likely to be lower than the
estimates derived from the Brune curve.
In total, we generated 130,000 inputs sets from the Sobol sequence and
simulated the resulting sediment supply to the delta for 17 portfolios (five
portfolios representing past dam development, six for the planned future,
and six for MDC futures), for a total of 2,210,000 runs.
We then use Sobol indices to determine the sensitivity of the response
variable (sediment supply) to each of the 12 model parameters. Both the
input sampling and the Sobol sensitivity analysis were implemented using the
MOEA Framework (50).
Data Availability. Data on dam location and design (25), delta topography (6),
and global projections of sediment yields (42) are available from repositories
associated with the respective publications. Color maps for Fig. 4 are avail-
able from Crameri (58). New geospatial data (geomorphic provinces) and code
and data required to reproduce the robust analysis for future dam portfolios
are available from Zenodo at https://doi.org/10.5281/zenodo.5138143 (59). All
other study data are included in the article and/or SI Appendix.
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Strategic basin and delta planning increases the resilience of the Mekong Delta under
future uncertainty
https://doi.org/10.1073/pnas.2026127118
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