Sea level rise (SLR) assessments are commonly used to identify the extent that coastal populations are at risk to

flooding. However, the data and assumptions used to develop these assessments contain numerous sources and types

of uncertainty, which limit confidence in the accuracy of modeled results. This study illustrates how the intersection

of uncertainty in digital elevation models (DEMs) and SLR lead to a wide range of modeled outcomes. SLR

assessments are then reviewed to identify the extent that uncertainty is documented in peer-reviewed articles. The

paper concludes by discussing priorities needed to further understand SLR impacts. (PDF contains 4 pages

Kettle, Nathan

English

Aquatic Commons

 IMPROVING REPORTING OF UNCERTAINTIES IN SEA LEVEL RISE ASSESSMENTS 
 
Nathan P. Kettle* 
 
 
Sea level rise (SLR) assessments are commonly used to identify the extent that coastal populations are at risk to 
flooding. However, the data and assumptions used to develop these assessments contain numerous sources and types 
of uncertainty, which limit confidence in the accuracy of modeled results. This study illustrates how the intersection 
of uncertainty in digital elevation models (DEMs) and SLR lead to a wide range of modeled outcomes. SLR 
assessments are then reviewed to identify the extent that uncertainty is documented in peer-reviewed articles. The 
paper concludes by discussing priorities needed to further understand SLR impacts. 
 
Uncertainty in sea level rise assessments 
 
There are many sources and types uncertainty that compromise the accuracy of SLR assessments. Errors may arise 
from measuring and monitoring sea level (1), determining trends (2), estimating trajectories of change (3), 
predicting social change (4), predicting shoreline change (5), and using inadequate data and methods to quantify 
impacts. These uncertainties limit confidence in SLR models (6, 7). 
 
DEMs are often used to evaluate SLR impacts in the absence of information on the location of future shorelines. 
Many sources of DEMs are available at local scales – each source having unique characteristics and data quality. 
USGS Level 1 (USGS L1) DEMs are frequently used to evaluate SLR impacts at local scales in the United States 
given that these data are computationally efficient, inexpensive to obtain, and higher resolution data may not be 
available. USGS L1 DEMs were developed as a standardized elevation data set for 7.5 minute quadrangles. 
Elevation units are provided in whole feet or meters, referenced to mean sea level, and have a 30 x 30 m spatial 
resolution. Errors in DEMs are a function of the collection process, processing, quality control of data, and 
geographic characteristics of the land (8). The root mean square error (RMSE) is frequently used to assess the 
vertical accuracy of DEMs as it enables a straightforward comparison of error with other DEMs and because it is 
difficult to obtain other information on the collection or processing of the data. All USGS L1 DEMs are required to 
have a RMSE value less than 15 meters, though less than seven meters is preferred (9).  
 
There are also many uncertainties in predicting the rate and magnitude of sea level change. Major uncertainties 
include predicting future greenhouse gas emissions, understanding the sensitivity of the climate to a doubling of 
carbon dioxide, and understanding ice dynamics (10). These factors contribute to global SLR predictions that range 
from 18 to 200 cm by the year 2100 (3, 11). The following section illustrates the extent that uncertainty in DEMs 
and SLR scenarios lead to a wide range of populations at risk to SLR.  
 
Compounding uncertainties: SLR scenarios and DEMs 
 
Nine simple inundation simulations were used to illustrate how uncertainty in DEMs and future SLR lead to 
different estimates of the population at risk throughout Charleston County, South Carolina. Each of the nine 
simulations represented a different combination of SLR change and elevation estimate from USGS L1 DEMs. 
 
Continuous elevation values throughout Charleston County were obtained from 32, USGS L1 DEMs. Three 
different elevation surfaces were used to account for the uncertainty in elevation. The first elevation surface was 
based on the reported elevation value, while the second and third elevation surfaces were based on two error budget 
models. Each error budget model was based on the reported RMSE for each quadrangle. RMSE values, which 
ranged from one to four meters, were added or subtracted from the reported elevation using a local function. Adding 
and subtracting the RMSE from the reported elevation value represents an underprediction and overprediction of the 
elevation surface, respectively. This procedure provides 68% confidence that the actual elevation value lies within 
the range of the two error budget models. Spatial stationary and normality are assumed for errors within each DEM.   
 
Three scenarios were used to represent the projected range of SLR by 2100. The lowest scenario (37.4 cm) was 
based on extrapolated sea level trends from the NOAA CO-OPS tidal gauge measurements in Charleston (12). The 
middle scenario (80 cm) represents an accelerated rate of SLR that accounts for plausible increases in ice flow 
dynamics (11). The high scenario (2 m) represents the upper end of accelerated SLR that could arise from increased 
 Table 1: Population at risk for different SLR scenarios and DEMs. 
ice flow (11). Census tract level data was distributed homogenously among each census tract to determine the 
population at risk (13). 
 
Results indicate that uncertainty within DEMs and SLR contributes to substantially different estimates of population 
at risk (Table 1). The uncertainty in USGS L1 DEMs alone contributes to estimates of population at risk that range 
from 2 to 104,000 people for the 37 cm SLR scenario. Results also illustrate the sensitivity of DEMs to different 
SLR scenarios. Specifically, these DEMs did not reveal a difference between the 37 and 80 cm SLR scenarios. This 
is because elevation units are reported in whole meters and thus lack the sensitivity to detect changes in sea level 
that that occur between integers.   
 
 
 
 
 
 
 
 
 
Documenting uncertainty in sea level rise assessments 
 
Documenting uncertainty is important to increasing the transparency within and comparability among SLR 
assessments. This section reviews 97 SLR assessments to evaluate the extent that uncertainties are reported in peer-
reviewed journals. The ISI Web of Knowledge search engine and keywords “sea level rise” and “vulnerability”, 
“adaptive capacity”, “resilience”, or “adaptation” were used to identify the sample. Among the 240 articles initially 
identified, these 97 specifically addressed SLR and were written in English. 
 
Many sources of uncertainty were documented within the sample of SLR assessments. The rate and magnitude of 
future SLR, climate change, human dimensions, and DEMs were the most frequently cited uncertainties. The human 
dimensions category included future changes in population, policy, and human responses to SLR.   
 
Documenting DEM characteristics used in SLR assessments is important to standardize findings and increase 
comparability among assessments. Seventy-three of the initial 240 assessments were selected to identify the 
reporting of DEM characteristics. Each of the 73 assessments used a DEM and SLR scenario to model SLR impacts. 
Three criteria were established to evaluate the appropriate reporting of DEM characteristics: documentation of the 
source, providing horizontal resolution, and providing vertical resolution. Accuracy of the DEM was not used as a 
criterion. Based on these criteria, only 10 assessments (13.7 percent of the total) met all 3 criteria for the appropriate 
use of DEMs (Figure 1). Although reporting each criterion steadily increased between 1992 and 2008, less than half 
of all studies reported the source of the DEMs and less than one-third of the studies mentioned the horizontal or 
vertical resolution.  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 Modeled population at risk to SLR 
SLR 
Scenario 
Overpredicted 
Elevation 
Reported 
Elevation 
Underpredicted 
Elevation 
37 cm 104,200 50,351 2 
80 cm 104,200 50,351 2 
2 m 166,621 98,365 32,646 
 Figure 1: DEM characteristics reported in SLR assessments. 
 
 
 
 
 
 
 
 
 
 
 
 
Discussion 
 
This study illustrated how uncertainties within two elements of SLR assessments can lead to a large range of 
modeled results. Specifically, estimates of population at risk to SLR ranged from 2 to 166,000. These findings 
demonstrate that results of SLR assessments are sensitive to the assumptions, choices, and methods selected for 
analysis. It also demonstrates the importance of using scenarios to bound uncertainty. 
 
Many SLR assessments do not document uncertainties, despite its notable role in affecting modeled outcomes. At a 
minimum, SLR assessments must include documentation on the assumptions regarding which SLR scenarios was 
used, the tidal datum selected (high or low tide), and the source, horizontal resolution, and vertical resolution of the 
DEM. Guidelines for the appropriate reporting of DEMs exist (14), yet many authors and editors do not adhere to 
these standards. These results suggest that it is not possible to evaluate the accuracy of the findings or compare 
results from one region or assessment to another.  Education and outreach efforts are also important to increase the 
capacity of decision makers to interpret SLR assessments and maps accurately (see, 15).  
 
Research Priorities 
 
The following two recommendations are provided to improve our understanding and communication of uncertainties 
in SLR assessments. 
 
1) Develop methods to improve existing DEMs. It may be possible to use land cover or other source data to 
improve existing DEMs, given that DEMs tend to over-predict elevation when modeling the canopy surface 
(8, 16).  
 
2) Improve representation of compounding uncertainties. This analysis may be extended by including other 
sources of uncertainty. For example, there are many ways to distribute population data among a defined 
area, including centroid-based, homogenous, and dasymetric approaches, and each of these techniques is 
likely to yield different estimates of population at risk (17). Further, many studies use static population data 
while projecting dynamic changes in sea level. This uncertainty analysis could be extended by using both 
static and dynamic population change estimates. There are also several other sources of uncertainty that are 
known, but cannot yet be quantified. These uncertainties include predicting shoreline change, 
understanding how humans will adapt to future conditions, and quantifying social, economic and biological 
impacts (4, 5, 18, 19). Methods and techniques to represent the extent that these uncertainties affect SLR 
assessments would be useful in judging risks and tradeoff 
 
Literature Cited  
 
1. P. L. Woodworth, Philosophical Transactions of the Royal Society A 364, 787 (2006). 
2. S. Jevrejeva, A. Grinsted, J. C. Moore, S. Holgate, Journal of Geophysical Research 11, C09012 (2006). 
 3. N. L. Bindoff et al., S. Solomon et al., Eds. (Cambridge University Press, Cambridge, United Kingdom and 
New York, NY, USA, 2007) pp. 385-432. 
4. S. C. Moser, Global Environmental Change 14, 353 (2005). 
5. J. M. Slott, A. B. Murray, A. D. Ashton, T. J. Crowley, Geophysical Research Letters 33, L18404 (2006). 
6. S. H. Schneider, in Social Science Research and Climate Change: In Interdisciplinary Appraisal R. S. 
Chen, E. Boulding, S. H. Schneider, Eds. (D. Reidel Publishing, Boston, 1983) pp. 9-15. 
7. A. Henderson-Sellers, Climatic Change 25, 203 (1993). 
8. M. E. Hodgson, J. R. Jensen, L. Schmidt, S. Schill, B. Davis, Remote Sensing of Environment 84, 295 
(2003). 
9. USGS, USGS Digital Elevation Model http://eros.usgs.gov/#/Guides/dem Accessed Feb 2010 (2010) 
10. R. B. Alley, P. U. Clark, P. Huybrechts, I. Joughin, Science 310, 456 (2005). 
11. W. T. Pfeffer, J. T. Harper, S. O'Neel, Science 321, 1340 (2008). 
12. NOAA CO-OPS, in Tides and Currents. (2009). 
13. U.S. Census Bureau. (U.S. Census Bureau, 2010). 
14. ASPRS, Vertical Accuracy Reporting for lidar data 
http://www.asprs.org/society/committees/lidar/Downloads/Vertical_Accuracy_Reporting_for_Lidar_Data.p
df Accessed Feb 2010 (2004) 
15. N. P. Kettle, S. Chhotray, K. Dow, J. Whitehead, in North Carolina Sea Level Rise Forum. (North Raleigh, 
NC, 2010). 
16. P. V. Bolstad, T. Stowe, Photogrammetric Engineering and Remote Sensing 60, 1327 (1994). 
17. M. Langford, G. Higgs, The Professional Geographer 58, 294 (2006). 
18. Heinz Center, The Hidden Costs of Coastal Hazards: Implications for Risk Assessment and Mitigation 
(Island Press, Washington, DC, 2000), pp. 252. 
19. G. Yohe, J. E. Neumann, P. Marshall, H. Amaden, Climatic Change 32, 387 (1996). 
 
 
Nathan P. Kettle 
University of South Carolina 
Department of Geography 
709 Bull Street  
Columbia, South Carolina 29208 
Ph: (803) 777-5234 
Fax: (803) 777-4972 


Improving reporting of uncertainties in sea level rise assessments

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Improving reporting of uncertainties in sea level rise assessments

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