FAU Geosciences

South Florida


Cooper HM (planned submission to Remote Sensing of Environment), Correcting LiDAR-derived Digital Elevation Models in the Coastal Everglades

Cooper Coastal Everglades


Cooper HM, Zhang C, Selch D (2015) Incorporating Uncertainty of Groundwater Modelling in Sea-Level Rise Assessment: A Case Study in South Florida. Climatic Change. doi: 10.1007/s10584-015-1334-1

Cooper SLR map


Researchers can assist in effective decision making by reducing uncertainty in marine and groundwater inundation due to Sea-level Rise (SLR). A majority of studies consider marine inundation, but only recently has groundwater inundation been incorporated into SLR mapping. However, the effect of including uncertainty in groundwater modeling is still not well understood. In this study, we evaluate the effect of considering groundwater modeling uncertainty in assessing inundated land area vulnerable to marine and groundwater inundation in South Florida. Six Water Table Elevation Model (WTEM) techniques (Multiple Linear Regression (MLR), Geographic Weighted Regression (GWR), Global Polynomial Interpolation (GPI), Inverse Distance Weighted (IDW), Ordinary Kriging (OK), and Empirical Bayesian Kriging (EBK)) are tested to identify the best approach. Simple inundation models excluding uncertainty with and without WTEM are examined. Refined inundation models using Monte Carlo simulation that include uncertainty in future SLR estimates, LiDAR elevation, vertical datums and the transformations made between them with and without WTEM uncertainty are evaluated. GPI and EBK are recognized as the best for producing WTEMs in two primary physiographic regions (the Southern Slope and Atlantic Coastal Ridge). Excluding uncertainty without WTEM underestimates total land area by 14%, while including uncertainty without WTEM overestimates total vulnerable land area by 16% at the 95% probability threshold. It is significant to include WTEM uncertainty in SLR vulnerability analysis for more effective adaptation decisions.


Cooper HM, 2014, Florida Center for Environmental Studies for project, "Potential inundation of Miami-Dade County under various climate change scenarios".


Decision makers in South Florida are faced with the problem of adapting to sea-level rise (SLR). High accuracy SLR vulnerability maps are critical because they illustrate potential assets at risk in their local communities. The purpose of this project is to generate high accuracy SLR vulnerability maps for 40 local elected officials in Miami-Dade County, FL. The mapping coastal inundation uncertainty approach by NOAA is extended to Monte Carlo simulation (following Cooper and Chen, 2013) to include uncertainty in LiDAR elevation data, vertical datums, and the transformations made between datums. The high end of Unified Southeast Florida SLR projections for planning purposes is utilized for years 2030 (7” or 18 cm) and 2060 (24” or 61 cm), respectively. Areas not hydrologically connected to the ocean are considered because they may be vulnerable to groundwater inundation due to higher water tables (Cooper et al, 2015 found it is better to map these areas to prevent further potential underestimation of vulnerable land area when compared to including groundwater modeling). These SLR maps help increase local decision makers' awareness of potential impacts due to future SLR as they are begining to consider adaptation strategies.

Miami Beach sea-level rise by year 2060

Hawai`i and Pacific Islands


Cooper HM and Chen Q (2013) Incorporating Uncertainty of Future Sea-Level Rise Estimates into Vulnerability Assessment: A Case Study in Kahului, Maui. Climatic Change. 121(4), p. 635-647.


Kahului sea-level rise mapAccurate sea-level rise (SLR) vulnerability assessments are essential in developing effective management strategies for coastal systems at risk. In this study, we evaluate the effect of combining vertical uncertainties in Light Detection and Ranging (LiDAR) elevation data, datum transformation and future SLR estimates on estimating potential land area and land cover loss, and whether including uncertainty in future SLR estimates has implications for adaptation decisions in Kahului, Maui. Monte Carlo simulation is used to propagate probability distributions through our inundation model, and the output probability surfaces are generalized as areas of high and low probability of inundation. Our results show that considering uncertainty in just LiDAR and transformation overestimates vulnerable land area by about 3% for the high probability threshold, resulting in conservative adaptation decisions, and underestimates vulnerable land area by about 14% for the low probability threshold, resulting in less reliable adaptation decisions for Kahului. Not considering uncertainty in future SLR estimates in addition to LiDAR and transformation has variable effect on SLR adaptation decisions depending on the land cover category and how the high and low probability thresholds are defined. Monte Carlo simulation is a valuable approach to SLR vulnerability assessments because errors are not required to follow a Gaussian distribution.


Cooper HM, Fletcher CH, Chen Q, Barbee MM (2013) Sea-Level Rise Vulnerability Mapping for Adaptation Decisions using LiDAR DEMs. Progress in Physical Geography. 37(6), p. 743-764.


Cooper et al., 2013Global sea-level rise (SLR) is projected to accelerate over the next century, with research indicating that global mean sea level may rise 18–48 cm by 2050, and 50–140 cm by 2100. Decision-makers, faced with the problem of adapting to SLR, utilize elevation data to identify assets that are vulnerable to inundation. This paper reviews techniques and challenges stemming from the use of Light Detection and Ranging (LiDAR) Digital Elevation Models (DEMs) in support of SLR decision-making. A significant shortcoming in the methodology is the lack of comprehensive standards for estimating LiDAR error, which causes inconsistent and sometimes misleading calculations of uncertainty. Workers typically aim to reduce uncertainty by analyzing the difference between LiDAR error and the target SLR chosen for decision-making. The practice of mapping vulnerability to SLR is based on the assumption that LiDAR errors follow a normal distribution with zero bias, which is intermittently violated. Approaches to correcting discrepancies between vertical reference systems for land and tidal datums may incorporate tidal benchmarks and a vertical datum transformation tool provided by the National Ocean Service (VDatum). Mapping a minimum statistically significant SLR increment of 32 cm is difficult to achieve based on current LiDAR and VDatum errors. LiDAR DEMs derived from ‘ground’ returns are essential, yet LiDAR providers may fail to remove returns over vegetated areas successfully. LiDAR DEMs integrated into a GIS can be used to identify areas that are vulnerable to direct marine inundation and groundwater inundation (reduced drainage coupled with higher water tables). Spatial analysis can identify potentially vulnerable ecosystems as well as developed assets. A standardized mapping uncertainty needs to be developed given that SLR vulnerability mapping requires absolute precision for use as a decision-making tool.



Cooper HM, Chen Q, Fletcher CH, Barbee MM (2012) Vulnerability Assessment due to Sea-Level Rise in Maui, Hawai`i using LiDAR Remote Sensing and GIS. Climatic Change 116 (3-4), p. 547-563.


Sea-level rise threatens islands and coastal communities due to vulnerable infrastructure and populations concentrated in low-lying areas. To assess the impacts of sea-level rise, elevation data of high spatial resolution and vertical accuracy are required. LiDAR (Light Detection and Ranging) data were used to produce high-resolution digital elevation models (DEM) for Kahului and Lahaina, Maui, to assess the impacts of sea-level rise. Two existing LiDAR datasets from USACE (U.S. Army Corps of Engineers) and NOAA (National Oceanic and Atmospheric Administration) were compared and calibrated using the Kahului Harbor tide station. Using tidal benchmarks is a valuable approach for vertically referencing LiDAR in areas lacking an established vertical datum. Exploratory analysis of the LiDAR point data demonstrated that improvements could be made to the USACE data, which was re-classified into ground returns (point data classified as ground after the removal of vegetation and buildings) for generating a DEM. Two sea-level rise scenarios of 0.75 to 1.9 m (Vermeer and Rahmstorf, 2009) were considered, and the DEMs were used to identify areas vulnerable to flooding. Our results indicate that under a sea-level rise scenario of 1.9 m, if no adaptive strategies are taken, a loss of about $325 million is likely due to the inundation of about 2.5 km2 of coastal area for Kahului, and a loss of about $515 million is likely due to the inundation of about 0.5 km2 of coastal area for Lahaina.