Controls on Algal Bloom Propagation in the Kuwait Bay Utilizing: An Integrated Remote Sensing and Statistical Approach

Thursday, 18 December 2014
Cameron J. Manche1, Mohamed Sultan2, Racha Elkadiri3, Saif Uddin4, Ahmad Al-Dousari5 and Kyle Chouinard1, (1)Western Michigan University, Kalamazoo, MI, United States, (2)Western Michigan Univ, Kalamazoo, MI, United States, (3)western michigan university, KALAMAZOO, MI, United States, (4)Kuwait Institute for Scientific Research, Environmental Sciences, Khaldya, Kuwait, (5)Kuwait University, Geography, Kuwait City, Kuwait
Algal blooms have become a major concern over the last decade in Kuwait’s coastal waters where these blooms caused massive fish kill in a number of incidences. The purpose of this study is to accomplish the following: 1) identify the factors controlling algal bloom the development and propagation using Aqua-MODIS satellite data products (from 07/2002 to 07/2012), (2) identify the spatial and temporal variations in Chlorophyll-a (Chl-a) production in relation to the controlling factors; and 3) develop conceptual and predictive models (using in-situ and satellite-based datasets) that account for reported historical blooms and can successfully predict algal bloom proliferation in space and time. To achieve these goals, the following tasks were accomplished: 1) in-situ Chl-a data was correlated with satellite-based (MODIS and MERIS) Chl-a data products (OC3M, GIOP, GSM, and OC4E); 2) Chl-a concentration (from OC3M) were correlated spatially and temporally with potential controlling factors (SST, Turbidity, Euphotic Depth, Precipitation, Photosynthetically Available Radiation, Wind Vectors etc.); 3) the stepwise regression method was applied to identify the most significant controlling factors and to determine their order of importance; and 4) a back-propagation artificial neural network (ANN) was constructed to predict the bloom occurrences in time (first layer process) and space (second layer process). Findings include: 1) the Aqua-MODIS OC3M Chlorophyll-a algorithm correlated best with in-situ measurements (RMSE: 2.42, Mean Bias: 32.2%); (2) maximum OC3M Chl-a concentration was observed throughout the months of August through October (Temp. range: 18.4° to 22.3 °C); 3) the stepwise regression identified SST, secchi disk depth, wind direction, OC3M and wind speed as the most indicative temporal factors (SST: most significant R2: 80.1%) and the OC3M, distance to shore, GSM, SST and GIOP as the most indicative spatial variables; 4) the ANN model showed an excellent prediction performance (area under receiver operating characteristic [ROC] curve: 0.99), and (5) findings are being utilized for the development of an early warning system in the Kuwait Bay.