Figure 5. Tide gauge predictions and observations. (a)
Maximum yearly change at each PSMSL RLR tide gauge site. Six sites have
a predicted a maximum yearly change of 5 mm or more. These six sites are
distinguished by a larger symbol; also indicated are the tide gauge
name, the maximum predicted yearly increase, and the year in which that
maximum occurred. (b) Predicted sea level change due to
reservoir construction (blue) versus RLR PSMSL observed (black dots) and
atmospheric corrected sea level (Piecuch et al., 2019; red
points) at the Baie Comeau, Québec, Canada tide gauge, near the
Manicouagan Reservoir. The reservoir was filled from 1970 to 1978. Units
on the vertical axis are millimeters, but the absolute value is
arbitrary. (c) As in (b), but for tide gauge in
Point-au-Père, Québec, Canada.
4.2 Variability in Reservoir Storage
Seepage is not the only mechanism that can alter the mass of a reservoir
after impoundment. An additional post-construction signal comes from
variations in the amount of water impounded, either seasonally, or in
long-term draw-down of the water. We assess both of these for a handful
of the largest reservoirs. Records of lake levels can be difficult to
obtain in a uniform manner, so we use changes in the surface height as
observed by satellite altimetry, provided by the USDA Global Reservoir
and Lake Elevation Database
(https://ipad.fas.usda.gov/cropexplorer/global_reservoir/) to estimate
the mass change. The satellites generally observed the largest
reservoirs two to three times a month, beginning in 1992 and continuing
through the study period. Because fluctuations in lake level are small
over the ~10-day timescale between satellite
observations, the behavior of the reservoir volume can be reasonably
well constrained by this method. Dramatic increases of impounded water
that happen over a shorter timescale are generally due to large
precipitation events, in which case the signal from the reservoir is
likely to be masked by a significant increase in groundwater and surface
water unrelated to the reservoir itself.
From the surface area and volume provided for each reservoir in the
GRanD database we calculate a mean depth. The altimetry data show
changes in altitude, but not absolute altitude, so we cannot use these
data to verify when the reservoir is full. Instead, we set the maximum
datum from the altimetry as the mean depth of the reservoir, assuming
that this represents a full reservoir. We then use every other
altimetric data point to estimate the fractional change of the mean
depth, and approximate this as the fractional change of the volume of
the reservoir. We discuss both long-term drawdown and seasonal changes
in water storage.
Lake Powell in the United States is an excellent target for detailed
investigation. First, high-resolution data are available to verify the
accuracy of the GRanD dataset. Second, satellite altimetry data show
that Lake Powell is characterized by a decline in lake level of slightly
more than 12 m between 2000 and 2011. Therefore, we can use the data set
to investigate the effects of a long-term reduction in water storage,
and whether the use of satellite altimetry is appropriate in determining
storage changes in large reservoirs.
GRanD lists Lake Powell as the 43rd largest reservoir
in the database, with a capacity of 25.07 km3 and a
surface area of 120.7 km2. In contrast, the Bureau of
Reclamation (BoR; part of the United States Department of the Interior),
which manages Lake Powell and the Glen Canyon Dam that impounds it,
lists the capacity as 32.3 km3 and the surface area as
688.9 km2 (https://www.usbr.gov/uc/rm/crsp/gc/). These
two sets of estimates produce very different values for the mean depth
of the reservoir, 208 m and 47 m respectively. The 12 m decline shown in
the satellite altimetry data, then, represents a reduction in impounded
water of either 6% or 25%. The BoR also provide daily calculations of
reservoir storage based on the observed elevation of the water level and
bathymetric surveys. For the time period 2000 to 2011, these surveys
(https://www.usbr.gov/rsvrWater/HistoricalApp.html) indicate that the
elevation in Lake Powell decreased 13 m from 1122 m to 1109 m, and that
storage in Lake Powell decreased from 27.1 km3 to 19.7
km3, a decrease of 27%. The satellite altimetry data
are thus consistent with the water impoundment reported by the BoR, and
additionally support the BoR-reported parameters for reservoir size,
rather than the values in the GRanD database.
While a decline of 27% in the volume of the reservoir does contribute
meaningfully to the GRD fingerprint through time, it is similar in
magnitude—but opposite in sign—to the seepage we assume for Lake
Powell (Fig. S4b). This time series indicates that significant,
long-term drawdown in impoundment can change the magnitude of local sea
level change associated with the unloading. However, for reservoirs that
do not experience significant drawdown in storage, periods of low water
levels will be characterized by a relatively small perturbation in local
sea level, and will have a second-order effect on the global fingerprint
of artificially impounded water.
Lake Guri, a reservoir in Venezuela with a volume of 135
km3, experiences seasonal variations in surface height
that routinely exceed 10 m, representing almost 30% of the reservoir’s
capacity as calculated from the GRanD-reported volume and surface area.
We compare sea level predictions—those that do and do not include
variability in Lake Guri—at the closest grid point to Lake Guri at in
Fig. S4a. Note that this location is not on the coast; because the coast
is at a greater distance, the impoundment signal will be smaller there.
The perturbations in sea level in this case are on the order of a few
millimeters, with higher sea level occurring when the reservoir is full,
as expected. If the seasonality is due to precipitation, i.e ., a
full reservoir occurs during a rainy season, then GRD effects associated
with the higher level of impoundment could enhance the hazard of coastal
flooding at precisely the time when increasing rain makes flooding more
likely.
4.3 Future Changes in Sea Level
Our projections of future changes in sea level due to water impoundment
are significantly larger than previous estimates. Following Rahmstorfet al ., (2012), Kopp et al . (2014) use impoundment data
from the WRD (Chao et al ., 2008) and population projections from
the United Nations Department of Economics and Social Affairs (2013) to
derive a relationship between cumulative impoundment and population (see
Supporting Information); their analysis implies a maximum additional
water impoundment corresponding to an additional GMSL fall of 6 mm. We
plot the future construction suggested by Zarfl et al . (2015)
alongside this relationship in Fig. S5, and show that the extrapolation
to 2040 based on Kopp et al . (2014) underestimates impoundment by
8.4 mm equivalent GMSL fall. There are uncertainties in our estimates
based on the Zarfl et al . (2015) database. However, it is clear
that the previously derived relationship between cumulative impoundment
and world population is likely to significantly underestimate future
artificial impoundment contributions to sea level changes.
Finally, we show in detail predictions for two areas that the Zarflet al . (2015) database indicates will experience significant
increases in reservoir impoundment: Southeast Asia and southeastern
Brazil (Fig. 4). These relatively low-elevation, highly populated areas
near the coast will experience a sea level rise due to impoundment of as
much as ~10 mm in the next two decades that will enhance
the hazard associated with global mean sea level rise. Vitousek et
al . (2017) suggest that in tropical areas, sea level rise of as little
as 50 mm can double the coastal flooding hazard. Thus, major reservoir
construction near such coasts could change the frequency of flooding in
these populated areas.
5 Conclusions
We present an estimate of spatially and temporally resolved sea level
change due to the impoundment of water in artificial reservoirs from
1900 to 2011, and a projection of sea level change to the year 2040 due
to the same effects. For each year over the period 1900 – 2011, our
predicted global GRD fingerprints are available by download from
doi.org/10.5281/zenodo.3751986.
Our analysis of historical data (Lehner et al. , 2011) is
consistent with previous studies (Chao et al ., 2008; Fiedler and
Conrad, 2010) that have estimated that globally averaged sea level fell
between 21 mm and 30 mm over the course of the twentieth century,
representing a significant fraction of the sea level budget. Our spatial
analysis is generally consistent with previous work (Fiedler and Conrad,
2010), but the spatio-temporal resolution of the predictions we present
allows for comparison of predictions to records from specific tide gauge
sites. Our estimate that reservoir impoundments could have raised sea
level by as much as 40 mm should motivate such studies.
Our analysis of reservoirs that are in some form of planning or
construction (Zarfl et al. , 2015) shows that the era of reservoir
construction has not ceased, that this continued impoundment will
contribute significantly to changes in sea level, and that the spatial
pattern of sea level change will be different from that of the majority
of the twentieth century. We show that current best projections may
underestimate by nearly a centimeter the effect of water impoundment on
GMSL in the next 20 years. It is difficult to accurately predict the sea
level fingerprint of reservoirs without precise estimates of the
volumetric capacity. Nonetheless, our calculations demonstrate that in
areas characterized by a high density of reservoir construction,
artificial impoundment is predicted to cause a local rise in sea level,
which may change the hazard of coastal flooding. This suggests that an
analysis of the local effects on sea level should be performed prior the
impoundment of large volumes of water in tropical areas of low
elevation, including Southeast Asia and southeastern Brazil.
Finally, our study highlights the importance of establishing a
comprehensive database of water impoundment that is volumetrically
complete, and geospatially and temporally referenced. Artificial water
impoundment is a crucial piece of the sea level budget (Cazenaveet al ., 2018; Kopp et al ., 2014, 2015), and accurately
accounting for its spatially and temporally varying contribution is
imperative. The database would be important not only for the community
concerned with the impacts of sea level rise, but also to a number of
others, including those focused on assessments of global electricity
generation (Zarfl et al. , 2015), the impact of river
fragmentation on ecosystems (Grill et al. , 2015), and hazards
related to reservoir-induced seismicity (e.g ., Gupta, 2002). In
the absence of such a database, our analysis represents the most
complete estimate to date of sea level change due to artificial
impoundment of water from 1900 to 2040.
Acknowledgments, Samples, and Data
The authors wish to thank C. Zarfl for the database of future
hydroelectric construction projects; and C. Piecuch, J. Fiedler, C.
Conrad, and B. Chao for supplying their models that we use to
contextualize our work. Satellite altimetry products courtesy of the
USDA/NASA G-REALM program
at https://ipad.fas.usda.gov/cropexplorer/global_reservoir/. Data for
reservoirs maintained by the U.S. Bureau of Reclamation, and accessed
via their website at https://www.usbr.gov. GRD Fingerprints for the
years 1900 – 2011 are provided at doi.org/10.5281/zenodo.3751986. GMT
was used to create many of the figures in this manuscript (Wesselet al ., 2013). Support for this work was provided by the NSF
(DGE-1106400 to WBH; ICER-1663807 to REK) and NASA (NSSC17K0698 to JXM;
80NSSC17K0698 to REK). Harvard University also provided funding to JXM.
References
Cazenave, A., & WCRP Global Sea Level Budget Group (2018). Global
sea-level budget 1993–present. Earth System Science Data ,10 (3), 1551–1590. https://doi.org/10.5194/essd-10-1551-2018
Chao, B. F., Wu, Y. H., & Li, Y. S. (2008). Impact of artificial
reservoir water impoundment on global sea level. Science ,320 (5873), 212–214. https://doi.org/10.1126/science.1154580
Chepurin, G. A., Carton, J. A., & Leuliette, E. (2014). Sea level in
ocean reanalyses and tide gauges. Journal of Geophysical Research:
Oceans , 119 (1), 147–155. https://doi.org/10.1002/2013JC009365
Compo, G. P., Whitaker, J. S., Sardeshmukh, P. D., Matsui, N., Allan, R.
J., Yin, X., et al. (2011). The Twentieth Century Reanalysis Project.Quarterly Journal of the Royal Meteorological Society ,137 (654), 1–28. https://doi.org/10.1002/qj.776
Dangendorf, S., Hay, C., Calafat, F. M., Marcos, M., Piecuch, C. G.,
Berk, K., & Jensen, J. (2019). Persistent acceleration in global
sea-level rise since the 1960s. Nature Climate Change ,9 (9), 705–710. https://doi.org/10.1038/s41558-019-0531-8
Dziewonski, A. M., & Anderson, D. L. (1981). Preliminary reference
Earth model. Physics of the Earth and Planetary Interiors ,25 (4), 297–356. https://doi.org/10.1016/0031-9201(81)90046-7
Fiedler, J. W., & Conrad, C. P. (2010). Spatial variability of sea
level rise due to water impoundment behind dams. Geophysical
Research Letters , 37 (12), 1–6.
https://doi.org/10.1029/2010GL043462
Gregory, J. M., Griffies, S. M., Hughes, C. W., Lowe, J. A., Church, J.
A., Fukimori, I., et al. (2019). Concepts and Terminology for Sea Level:
Mean, Variability and Change, Both Local and Global. Surveys in
Geophysics , 40 (6), 1251–1289.
https://doi.org/10.1007/s10712-019-09525-z
Grill, G., Lehner, B., Lumsdon, A. E., MacDonald, G. K., Zarfl, C., &
Reidy Liermann, C. (2015). An index-based framework for assessing
patterns and trends in river fragmentation and flow regulation by global
dams at multiple scales. Environmental Research Letters ,10 (1), 015001. https://doi.org/10.1088/1748-9326/10/1/015001
Gupta, H. K. (2002). A review of recent studies of triggered earthquakes
by artificial water reservoirs with special emphasis on earthquakes in
Koyna, India. Earth-Science Reviews , 58 (3–4), 279–310.
https://doi.org/10.1016/S0012-8252(02)00063-6
Harr, M. E. (Ed.). (1962). Groundwater and Seepage . New York:
McGraw-Hill.
Holgate, S. J., Matthews, A., Woodworth, P. L., Rickards, L. J.,
Tamisiea, M. E., Bradshaw, E., et al. (2013). New Data Systems and
Products at the Permanent Service for Mean Sea Level. Journal of
Coastal Research , 288 , 493–504.
https://doi.org/10.2112/JCOASTRES-D-12-00175.1
Kendall, R. A., Mitrovica, J. X., & Milne, G. A. (2005). On
post-glacial sea level - II. Numerical formulation and comparative
results on spherically symmetric models. Geophysical Journal
International , 161 (3), 679–706.
https://doi.org/10.1111/j.1365-246X.2005.02553.x
Kopp, R. E., Horton, R. M., Little, C. M., Mitrovica, J. X.,
Oppenheimer, M., Rasmussen, D. J., et al. (2014). Probabilistic 21st and
22nd century sea-level projections at a global network of tide-gauge
sites. Earth’s Future , 2 (8), 383–406.
https://doi.org/10.1002/2014ef000239
Kopp, R. E., Hay, C. C., Little, C. M., & Mitrovica, J. X. (2015).
Geographic Variability of Sea-Level Change. Current Climate Change
Reports , 1 (3), 192–204.
https://doi.org/10.1007/s40641-015-0015-5
Lehner, B., Liermann, C. R., Revenga, C., Vörösmarty, C. J., Fekete, B.
M., Crouzet, P., Döll, P., Endejan, M., & Frenken, K. (2011). Global
Reservoir and Dam (GRanD) database. European Environment ,
(March), 12. https://doi.org/10.7927/H4HH6H08
Lehner, B., Liermann, C. R., Revenga, C., Vörösmarty, C. J., Fekete, B.
M., Crouzet, P., Döll, P., Endejan, M., Frenken, K., et al. (2011).
High-resolution mapping of the world’s reservoirs and dams for
sustainable river-flow management. Frontiers in Ecology and the
Environment , 9 (9), 494–502. https://doi.org/10.1890/100125
Loo, T. (2007). Disturbing the Peace: Environmental Change and the
Scales of Justice on a Northern River. Environmental History ,12 (4), 895–919. https://doi.org/10.1093/envhis/12.4.895
Mitrovica, J.X. & Milne, G.A. (2003). On post-glacial sea level: I.
General theory. Geophys. J. Int., 154 , 253-267.
Mitrovica, J.X., Gomez, N., Morrow, E., Hay, C., Latychev, K. , &
Tamisiea, M.E. (2011). On the robustness of predictions of sea-level
fingerprints. Geophys. J. Int., 187 , 729-742.
Piecuch, C. G., Calafat, F. M., Dangendorf, S., & Jordà, G. (2019).The Ability of Barotropic Models to Simulate Historical Mean Sea
Level Changes from Coastal Tide Gauge Data . Surveys in
Geophysics (Vol. 40). Springer Netherlands.
https://doi.org/10.1007/s10712-019-09537-9
Piecuch, C. G., Huybers, P., & Tingley, M. P. (2017). Comparison of
full and empirical Bayes approaches for inferring sea-level changes from
tide-gauge data. Journal of Geophysical Research: Oceans ,122 (3), 2243–2258. https://doi.org/10.1002/2016JC012506
Poli, P., Hersbach, H., Tan, D., Dee, D., Thépaut, J.-N., Simmons, A.,
et al. (2013). The data assimilation system and initial performance
evaluation of the ECMWF pilot reanalysis of the 20th-century assimi-
lating surface observations only (ERA-20C). ERA Report series 14.
Rahmstorf, S., Perrette, M., & Vermeer, M. (2012). Testing the
robustness of semi-empirical sea level projections. Climate
Dynamics , 39 (3–4), 861–875.
https://doi.org/10.1007/s00382-011-1226-7
Vitousek, S., Barnard, P. L., Fletcher, C. H., Frazer, N., Erikson, L.,
& Storlazzi, C. D. (2017). Doubling of coastal flooding frequency
within decades due to sea-level rise. Scientific Reports ,7 (1), 1399. https://doi.org/10.1038/s41598-017-01362-7
Vörösmarty, C. J., Sharma, K. P., Fekete, B. M., Copeland, A. H.,
Holden, J., Marble, J., & Lough, J. A. (1997). The Storage and Aging of
Continental Runoff in Large Reservoir Systems of the World.Ambio , 26 (4), 210–219.
Wessel, P., Smith, W. H. F., Scharroo, R., Luis, J., & Wobbe, F.
(2013). Generic Mapping Tools: Improved Version Released. Eos,
Transactions American Geophysical Union , 94 (45), 409–410.
https://doi.org/10.1002/2013EO450001
United Nations, Department of Economic and Social Affairs, Population
Division (2013). World Population Prospects: The 2012 Revision,
Highlights and Advance Tables. Working Paper No. ESA/P/WP.228.
Zarfl, C., Lumsdon, A. E., Berlekamp, J., Tydecks, L., & Tockner, K.
(2015). A global boom in hydropower dam construction. Aquatic
Sciences , 77 (1), 161–170.
https://doi.org/10.1007/s00027-014-0377-0