Ocean Dynamics (2010) 60:1307–1318 DOI 10.1007/s10236-010-0319-x On the dynamics and morphology of extensive tidal mudflats: Integrating remote sensing data with an inundation model of Cook Inlet, Alaska Tal Ezer & Hua Liu Received: 13 April 2010 /Accepted: 11 July 2010 /Published online: 23 July 2010 # Springer-Verlag 2010 Abstract A new method of integrating satellite remote 1 Introduction sensing data and inundation models allows the mapping of extensive tidal mudflats in a sub-Arctic estuary, Cook Inlet Coastal regions are continuously being changed by man- (CI), Alaska. The rapid movement of the shorelines in CI made shore developments and natural impacts like due to the large tides (~10 m range) is detected from a vegetation changes, land and sea sediment movements, series of Landsat imagery taken at different tidal stages, sea-level rise, and in some cases by catastrophic events whereas GIS tools are used to identify the water coverage such as tropical storm surges or tsunamis. It is thus in each satellite image and to extract the coordinates of the critical to have flood prediction models that can simulate shoreline. Then, water level along the shoreline for each and possibly predict the impact of catastrophic events satellite image is calculated from the observed water level such as the flooding of New Orleans in 2005 by Katrina at Anchorage and the statistics of an inundation model. (Travis 2005) or the 2004 tsunami in the Indian Ocean Several applications of the analysis are demonstrated: 1. (Kowalik et al. 2006). Flood (or inundation) numerical studying the dynamics of a tidal bore and the flood/ebb models require, in addition to atmospheric forcing, processes, 2. identifying climatic changes in mudflats detailed coastal and land topography data. However, in morphology, and 3. mapping previously unobserved mud- many coastal regions, detailed high-resolution topographic flat topographies in order to improve inundation models. data are either not available, or the land is being changed The method can be used in other regions to evaluate models by human development or natural climatic impacts. Very and improve predictions of catastrophic floods such as high-resolution (~1 m) flood-inundation maps from air- those associated with hurricane storm surges and tsunamis. borne LiDAR data can be very useful (Bales et al. 2007; Zhou 2009), but these data are costly and not easily available Keywords Remote sensing . Ocean modeling . Alaska . everywhere on the globe. Therefore, this study aims to Tides develop new methodologies that use publicly available satellite remote sensing data to study the dynamics and morphology of flood zones that are otherwise unobserved by direct measurements (for example, mudflats are often too Responsible Editor: Joerg-Olaf Wolff dangerous for making shipboard ocean measurements). T. Ezer (*) Satellite imagery as well as LiDAR and aerial photography Center for Coastal Physical Oceanography, are often used qualitatively for catastrophic flooding to show Old Dominion University, “before and after” pictures, however, in this study we would 4111 Monarch Way, Norfolk, VA 23508, USA like to demonstrate a more quantitative use of satellite remote e-mail: [email protected] sensing data to actually map changing coastlines and morphology and potentially incorporate the new data in flood H. Liu prediction models. Department of Political Science and Geography, Old Dominion University, The rare occurrence and unpredictable nature of cata- Norfolk, VA 23529, USA strophic events make it difficult to study the impact of 1308 Ocean Dynamics (2010) 60:1307–1318 extreme flooding events. Alternatively, one may use locations An inundation model has been developed for CI (Oey et where flooding processes occur naturally as a test-bed al. 2007; Ezer et al. 2008), but its flood prediction laboratory to evaluate flood prediction and observation capability is limited by a lack of accurate topography data techniques. One such place is Cook Inlet (CI), Alaska, a for setting up the model and by a lack of observations of sub-Arctic estuary with extremely large tides of ~10 m range water coverage to validate the model. For example, Ezer and energetic tidal bores and rip currents (Oey et al. 2007; and Liu (2009) compared the topography data currently Fig. 1). CI is subject to coastal population development available for the CI inundation model (Oey et al. 2007) (about half of Alaska’s population lives in Anchorage and with Landsat remote sensing data, demonstrating the much around Cook Inlet) and sensitive ecosystems which might be greater details that the remote sensing data can provide. So affected by climate change. For example, the (recently how can one use remote sensing data to study morphology declared “Endangered Species”) population of CI’sbeluga and climatic changes, as well as improve flood prediction whales is declining, and may be affected by climate change models? The idea is to use geographic information system and coastal development (Ezer et al. 2008). The morphology (GIS) tools to combine numerical inundation models with of the region has been affected in the past by geological remote sensing data. Preliminary feasibility studies (Oey et events such as the 9.2 magnitude earthquakes of 1964 (the al. 2007; Ezer and Liu 2009; Liu and Ezer 2009), using most powerful earthquake recorded in North America) which Landsat and moderate resolution imaging spectroradiometer moved land areas by a few meters; associated tsunami (MODIS) data demonstrate that the moving shorelines due caused severe damage in Prince William Sound nearby. to the tidal ebbing and flooding cycle can be inferred from Large regions around Anchorage and Cook Inlet have not satellite data. While satellite data such as Landsat can been mapped and are not accessible to direct observations. identify flooded/exposed mudflats regions and provide a For example, the topographies of hundreds square kilometers two-dimensional picture of the water coverage, to describe of mudflats are largely unknown, though there are evidences the three-dimensional structure of the flooded zone, one that the morphology has been changed by the earthquake and needs to take into account the water level elevation (or continuous sediment movement. sea surface height, SSH). In CI, at particular times in the tidal cycle, water level may change by as much as ~10 m over ~50 km distance (Oey et al. 2007). The spatial water Cook Inlet Model Domain and Topography 350 level changes has not been addressed in the previous studies of remote sensing data (Ezer and Liu 2009;Liu Knik Arm 300 Anchorage and Ezer 2009), thus the need to integrate the satellite data 61N with the inundation model. Turnagain 250 Arm The goal of the study is to develop the methodology to combine remote sensing data, sea-level observations and 200 dynamic models in order to study the short- and long-term environmental impact of flooding and morphological 60N changes. Eventually, the new analysis can also be used to 150 Y (km) evaluate and improve inundation models used for flood prediction in other regions. 100 The paper is organized as follows. First in Section 2, the CI inundation model and the statistical flood prediction 50 59N Gulf of method are described and then in Section 3, the remote tides Alaska sensing analysis is described. The results of merging the 0 Kodiak Island satellite-derived data with the water level prediction are 154W 152W 150W presented in Section 4, and discussions and conclusions are -150 -100 -50 0 50 100 150 200 offered in Section 5. X (km) flood zone } Depth (m) 2 Inundation modeling and water level prediction -110 -90 -70 -50 -30 -10 MSL Fig. 1 The CI model domain and topography (see Oey et al. 2007, for 2.1 The Cook Inlet numerical model details). The depth (blue to red colors) is relative to the local mean sea level; gray areas are always dry land while magenta areas are Inundation modeling referred to ocean models that include inundation regions that can be wet or dry. Tidal forcing applied at the open boundaries on both sides of Kodiak Island. The area of interest wetting and drying (WAD) algorithms that allow a movable of this study is the boxed region in the upper inlet land-water boundary. Unlike most oceanic general circulation Ocean Dynamics (2010) 60:1307–1318 1309 Fig. 2 Comparison of the (a) Model SSH (2h after max) (c) Model SSH (6h after max) cm analytical water level (WL) 500 prediction (Eq. 1) with the sea 3 400 surface height (SSH) of the full three-dimensional numerical 61.4 2 61.4 exposed 300 mudflats model. The numerical model 200 results for a high tide and c low 1 100 tide are compared with the 61.2 61.2 analytical predictions in b and d, 0 respectively. e Hourly time evolution of the predicted WL -100 4 (open circle and asterisk) are 61 5 61 -200 compared with the model SSH 6 7 -300 for Anchorage (point #1 in a; 8 -400 blue line) and for the upper Knik 60.8 60.8 Arm (point #3 in a; green line) -500 -150.5 -150 -149.5 -149 -150.5 -150 -149.5 -149 (b) Predicted SSH (2h after max) (d) Predicted SSH (6h after max) 500 Latitude (N) 400 61.4 61.4 300 200 100 61.2 61.2 0 -100 61 61 -200 -300 -400 60.8 60.8 -500 -150.5 -150 -149.5 -149 -150.5 -150 -149.5 -149 Longitude (W) (e) Dynamic Model (solid lines) and Analytical Prediction ("o" a t#1; "*" at #3) 400 #1 #3 200 0 Water Level (cm) -200 -400 10 20 30 40 50 60 Time (h) models that have a wall-like fixed coastal boundary, models developed by Oey (2005, 2006),whichwasbasedonthe with WAD can simulate flooding of land areas or exposing of community Princeton Ocean Model (i.e., version POM- shallow water-covered areas.