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The coastal seas of China are increasingly threatened by algal blooms, yet their comprehensive spatiotemporal mapping and understanding of underlying drivers remain challenging due to high turbidity and heterogeneous water conditions. We developed a singular value decomposition-based algorithm to map these blooms using two decades of MODIS-Aqua satellite data, spanning from 2003 to 2022. Our findings indicate significant algal activity along the Chinese coastline, impacting an average annual area of approximately 1.8 × 105 km2. The blooms exhibit peak intensity in August, while the maximum affected area occurs in September, featuring multifrequency outbreaks in spring, and pronounced large-scale events in summer and autumn. Notably, our analysis demonstrates a robust 67% increase in bloom occurrences over the study period. This expansion is primarily attributed to increased nutrient inflow from terrestrial sources linked to human activity and precipitation, compounded by rising global sea surface temperatures. These spatiotemporal insights are critical for devising effective management strategies and policies to mitigate the impacts of algal blooms.
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Eutrofización , China , Océanos y Mares , Monitoreo del Ambiente , Estaciones del Año , Análisis Espacio-TemporalRESUMEN
As the Earth's temperature continues to rise, coral bleaching events become more frequent. Some of the most affected reef ecosystems are located in poorly monitored waters, and thus, the extent of the damage is unknown. We propose the use of marine heatwaves (MHWs) as a new approach for detecting coral reef zones susceptible to bleaching, using the Red Sea as a model system. Red Sea corals are exceptionally heat-resistant, yet bleaching events have increased in frequency. By applying a strict definition of MHWs on >30 year satellite-derived sea surface temperature observations (1985-2015), we provide an atlas of MHW hotspots over the Red Sea coral reef zones, which includes all MHWs that caused major coral bleaching. We found that: (a) if tuned to a specific set of conditions, MHWs identify all areas where coral bleaching has previously been reported; (b) those conditions extended farther and occurred more often than bleaching was reported; and (c) an emergent pattern of extreme warming events is evident in the northern Red Sea (since 1998), a region until now thought to be a thermal refuge for corals. We argue that bleaching in the Red Sea may be vastly underrepresented. Additionally, although northern Red Sea corals exhibit remarkably high thermal resistance, the rapidly rising incidence of MHWs of high intensity indicates this region may not remain a thermal refuge much longer. As our regionally tuned MHW algorithm was capable of isolating all extreme warming events that have led to documented coral bleaching in the Red Sea, we propose that this approach could be used to reveal bleaching-prone regions in other data-limited tropical regions. It may thus prove a highly valuable tool for policymakers to optimize the sustainable management of coastal economic zones.
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Antozoos , Arrecifes de Coral , Animales , Ecosistema , Océano Índico , TemperaturaRESUMEN
Global climate change has profound implications on species distributions and ecosystem functioning. In the coastal zone, ecological responses may be driven by various biogeochemical and physical environmental factors. Synergistic interactions can occur when the combined effects of stressors exceed their individual effects. The Red Sea, characterized by strong gradients in temperature, salinity, and nutrients along the latitudinal axis provides a unique opportunity to study ecological responses over a range of these environmental variables. Using multiple linear regression models integrating in situ, satellite and oceanographic data, we investigated the response of coral reef taxa to local stressors and recent climate variability. Taxa and functional groups responded to a combination of climate (temperature, salinity, air-sea heat fluxes, irradiance, wind speed), fishing pressure and biogeochemical (chlorophyll a and nutrients - phosphate, nitrate, nitrite) factors. The regression model for each species showed interactive effects of climate, fishing pressure and nutrient variables. The nature of the effects (antagonistic or synergistic) was dependent on the species and stressor pair. Variables consistently associated with the highest number of synergistic interactions included heat flux terms, temperature, and wind speed followed by fishing pressure. Hard corals and coralline algae abundance were sensitive to changing environmental conditions where synergistic interactions decreased their percentage cover. These synergistic interactions suggest that the negative effects of fishing pressure and eutrophication may exacerbate the impact of climate change on corals. A high number of interactions were also recorded for algae, however for this group, synergistic interactions increased algal abundance. This study is unique in applying regression analysis to multiple environmental variables simultaneously to understand stressor interactions in the field. The observed responses have important implications for understanding climate change impacts on marine ecosystems and whether managing local stressors, such as nutrient enrichment and fishing activities, may help mitigate global drivers of change.
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Antozoos , Arrecifes de Coral , Animales , Clorofila A , Cambio Climático , Ecosistema , Océano ÍndicoRESUMEN
In this work, two Polynomial Chaos (PC) surrogates were generated to reproduce Monte Carlo (MC) molecular simulation results of the canonical (single-phase) and the NVT-Gibbs (two-phase) ensembles for a system of normalized structureless Lennard-Jones (LJ) particles. The main advantage of such surrogates, once generated, is the capability of accurately computing the needed thermodynamic quantities in a few seconds, thus efficiently replacing the computationally expensive MC molecular simulations. Benefiting from the tremendous computational time reduction, the PC surrogates were used to conduct large-scale optimization in order to propose single-site LJ models for several simple molecules. Experimental data, a set of supercritical isotherms, and part of the two-phase envelope, of several pure components were used for tuning the LJ parameters (ε, σ). Based on the conducted optimization, excellent fit was obtained for different noble gases (Ar, Kr, and Xe) and other small molecules (CH4, N2, and CO). On the other hand, due to the simplicity of the LJ model used, dramatic deviations between simulation and experimental data were observed, especially in the two-phase region, for more complex molecules such as CO2 and C2 H6.
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Changes in phytoplankton dynamics influence marine biogeochemical cycles, climate processes, and food webs, with substantial social and economic consequences. Large-scale estimation of phytoplankton biomass was possible via ocean colour measurements from two remote sensing satellites - the Coastal Zone Colour Scanner (CZCS, 1979-1986) and the Sea-viewing Wide Field-of-view Sensor (SeaWiFS, 1998-2010). Due to the large gap between the two satellite eras and differences in sensor characteristics, comparison of the absolute values retrieved from the two instruments remains challenging. Using a unique in situ ocean colour dataset that spans more than half a century, the two satellite-derived chlorophyll-a (Chl-a) eras are linked to assess concurrent changes in phytoplankton variability and bloom timing over the Northeast Atlantic Ocean and North Sea. Results from this unique re-analysis reflect a clear increasing pattern of Chl-a, a merging of the two seasonal phytoplankton blooms producing a longer growing season and higher seasonal biomass, since the mid-1980s. The broader climate plays a key role in Chl-a variability as the ocean colour anomalies parallel the oscillations of the Northern Hemisphere Temperature (NHT) since 1948.
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Clorofila/análisis , Cambio Climático , Eutrofización , Fitoplancton/fisiología , Tecnología de Sensores Remotos/instrumentación , Nave Espacial/instrumentación , Océano Atlántico , Clorofila A , Color , Mar del Norte , Estaciones del Año , Factores de TiempoRESUMEN
Underwater noise pollution is a significant environmental issue that can have detrimental effects on marine ecosystems. One of the main sources of underwater noise pollution is ship traffic, which has been shown to negatively impact marine animals by masking communication signals and altering their behaviors. This study represents the first comprehensive analysis of underwater ship noise in the Red Sea, wherein noise maps of ships sailing through the main shipping lane in the Red Sea were simulated by integrating both anthropogenic and environmental variables. These maps offer valuable insights for policymakers, enabling them to make informed decisions and implement targeted mitigation efforts.
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This study examines the spatiotemporal variability of drought and associated physical processes over the Arabian Peninsula (AP). For this purpose, we computed the standardized precipitation evapotranspiration index (SPEI) for the period 1951-2020 using the Climate Research Unit and fifth generation ECMWF atmospheric reanalysis datasets. By applying rotated empirical orthogonal function analysis on the SPEI data, we identified four homogeneous and coherent drought regions. The droughts in the northern regions follow a relatively similar temporal evolution as compared to those in the southern region. All four sub-regions of the AP exhibit a significant drying trend (p < 0.01) with an abrupt acceleration in drought frequency and intensity over the last two decades. The increase in droughts is associated with the reduction of synoptic activity and an increase in the high pressure over the AP. Seasonally, potential evapotranspiration is the dominant driver of summer droughts in the AP, whereas both precipitation and temperature are important for driving winter droughts. The summer droughts, mainly over the northern AP, are due to the occurrence of an anomalous equivalent barotropic high associated with anomalous dry and hot conditions. However, anomalous dry conditions in winter are a result of an anomalous paucity of winter storms caused by the weakening of the sub-tropical jets.
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A risk analysis is conducted considering an array of release sources located around the NEOM shoreline. The sources are selected close to the coast and in neighboring regions of high marine traffic. The evolution of oil spills released by these sources is simulated using the MOHID model, driven by validated, high-resolution met-ocean fields of the Red Sea. For each source, simulations are conducted over a 4-week period, starting from first, tenth and twentieth days of each month, covering five consecutive years. A total of 180 simulations are thus conducted for each source location, adequately reflecting the variability of met-ocean conditions in the region. The risk associated with each source is described in terms of amount of oil beached, and by the time required for the spilled oil to reach the NEOM coast, extending from the Gulf of Aqaba in the North to Duba in the South. To further characterize the impact of individual sources, a finer analysis is performed by segmenting the NEOM shoreline, based on important coastal development and installation sites. For each subregion, source and release event considered, a histogram of the amount of volume beached is generated, also classifying individual events in terms of the corresponding arrival times. In addition, for each subregion considered, an inverse analysis is conducted to identify regions of dependence of the cumulative risk, estimated using the collection of all sources and events considered. The transport of oil around the NEOM shorelines is promoted by chaotic circulations and northwest winds in summer, and a dominant cyclonic eddy in winter. Hence, spills originating from release sources located close to the NEOM shorelines are characterized by large monthly variations in arrival times, ranging from less than a week to more than 2 weeks. Similarly, large variations in the volume fraction of beached oil, ranging from less then 50% to more than 80% are reported. The results of this study provide key information regarding the location of dominant oil spill risk sources, the severity of the potential release events, as well as the time frames within which mitigation actions may need to deployed.
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Modeling and predicting air pollution concentrations is important to provide early warnings about harmful atmospheric substances. However, uncertainty in the dynamic process and limited information about chemical constituents and emissions sources make air-quality predictions very difficult. This study proposed a novel deep-learning method to extract high levels of abstraction in data and capture spatiotemporal features at hourly and daily time intervals in NEOM City, Saudi Arabia. The proposed method integrated a residual network (ResNet) with the convolutional long short-term memory (ConvLSTM). The ConvLSTM method was boosted by a ResNet model for deeply extracting the spatial features from meteorological and pollutant data and thereby mitigating the loss of feature information. Then, health risk assessment was put forward to evaluate PM10 and PM2.5 risk sensitivity in five districts in NEOM City. Results revealed that the proposed method with effective feature extraction could greatly optimize the accuracy of spatiotemporal air quality forecasts compared to existing state-of-the-art models. For the next hour prediction tasks, the PM10 and PM2.5 of MASE were 9.13 and 13.57, respectively. The proposed method provides an effective solution to improve the prediction of air-pollution concentrations while being portable to other regions around the world.
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Contaminantes Atmosféricos , Contaminación del Aire , Contaminantes Atmosféricos/análisis , Material Particulado/análisis , Monitoreo del Ambiente/métodos , Contaminación del Aire/análisis , Medición de Riesgo , PredicciónRESUMEN
In this paper, supplementary cementitious materials are used as a substitute for cement to decrease carbon dioxide emissions. A by-product of the iron manufacturing industry, ground granulated blast-furnace slag (GGBS), known to improve some performance characteristics of concrete, is used as an effective cement replacement to manufacture mortar samples. Here, the influence of curing conditions on the durability of samples including various amounts of GGBS is investigated experimentally and numerically. Twelve high-strength Portland cement CEM I 52.5 N samples were prepared, in which 0%, 45%, 60%, and 80% of cement were substituted by GGBS. In addition, three curing conditions (standard, dry, and cold curing) were applied to the samples. Durability aspects were studied through porosity, permeability, and water absorption. Experimental results indicate that samples cured in standard conditions gave the best performance in comparison to other curing conditions. Furthermore, samples incorporating 45% of GGBS have superior durability properties. Permeability and water absorption were improved by 17% and 18%, respectively, compared to the reference sample. Thereafter, data from capillary suction experiments were used to numerically determine the hydraulic properties based on a Bayesian inversion approach, namely the Markov Chain Monte Carlo method. Finally, the developed numerical model accurately estimates the hydraulic characteristics of mortar samples and greatly matches the measured water inflow over time through the samples.
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The interdependence between the water and power sectors is a growing concern as the need for desalination increases globally. Therefore, co-optimizing interdependent systems is necessary to understand the impact of one sector on another. We propose a framework to identify the optimal investment mix for a co-optimized water-power system and apply it to Neom, Saudi Arabia. Our results show that investment strategies that consider the co-optimization of both systems result in total cost savings for the power sector compared to independent approaches. Analysis results suggest that systems with higher shares of non-dispatchable renewables experience the most significant cost reductions.
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Energía Renovable , Purificación del Agua , Arabia Saudita , Agua , Purificación del Agua/métodosRESUMEN
Mangrove ecosystems represent one of the most effective natural environments for fixing and storing carbon (C). Mangroves also offer significant co-benefits, serving as nurseries for marine species, providing nutrients and food to support marine ecosystems, and stabilizing coastlines from erosion and extreme events. Given these considerations, mangrove afforestation and associated C sequestration has gained considerable attention as a nature-based solution to climate adaptation (e.g., protect against more frequent storm surges) and mitigation (e.g. offsetting other C-producing activities). To advance our understanding and description of these important ecosystems, we leverage Landsat-8 and Sentinel-2 satellite data to provide a current assessment of mangrove extent within the Red Sea region and also explore the effect of spatial resolution on mapping accuracy. We establish that Sentinel-2 provides a more precise spatial record of extent and subsequently use these data together with a maximum entropy (MaxEnt) modeling approach to: i) map the distribution of Red Sea mangrove systems, and ii) identify potential areas for future afforestation. From these current and potential mangrove distribution maps, we then estimate the carbon sequestration rate for the Red Sea (as well as for each bordering country) using a meta-analysis of sequestration values surveyed from the available literature. For the mangrove classification, we obtained mapping accuracies of 98 %, with a total Red Sea mangrove extent estimated at approximately 175 km2. Based on the MaxEnt approach, which used soil physical and environmental variables to identify the key factors limiting mangrove growth and distribution, an area of nearly 410 km2 was identified for potential mangrove afforestation expansion. The factors constraining the potential distribution of mangroves were related to soil physical properties, likely reflecting the low sediment load and limited nutrient input of the Red Sea. The current rate of carbon sequestration was calculated as 1034.09 ± 180.53 Mg C yr-1, and the potential sequestration rate as 2424.49 ± 423.26 Mg C yr-1. While our results confirm the maintenance of a positive trend in mangrove growth over the last few decades, they also provide the upper bounds on above ground carbon sequestration potential for the Red Sea mangroves.
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Ecosistema , Rhizophoraceae , Carbono , Secuestro de Carbono , Océano Índico , Suelo , HumedalesRESUMEN
We describe an approach to partition a vertical profile of chlorophyll-a concentration into contributions from two communities of phytoplankton: one (community 1) that resides principally in the turbulent mixed-layer of the upper ocean and is observable through satellite visible radiometry; the other (community 2) residing below the mixed-layer, in a stably stratified environment, hidden from the eyes of the satellite. The approach is tuned to a time-series of profiles from a Biogeochemical-Argo float in the northern Red Sea, selected as its location transitions from a deep mixed layer in winter (characteristic of vertically well-mixed systems) to a shallow mixed layer in the summer with a deep chlorophyll-a maximum (characteristic of vertically stratified systems). The approach is extended to reproduce profiles of particle backscattering, by deriving the chlorophyll-specific backscattering coefficients of the two communities and a background coefficient assumed to be dominated by non-algal particles in the region. Analysis of the float data reveals contrasting phenology of the two communities, with community 1 blooming in winter and 2 in summer, community 1 negatively correlated with epipelagic stratification, and 2 positively correlated. We observe a dynamic chlorophyll-specific backscattering coefficient for community 1 (stable for community 2), positively correlated with light in the mixed-layer, suggesting seasonal changes in photoacclimation and/or taxonomic composition within community 1. The approach has the potential for monitoring vertical changes in epipelagic biogeography and for combining satellite and ocean robotic data to yield a three-dimensional view of phytoplankton distribution.
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In this study, the variability and trends of the outdoor thermal discomfort index (DI) in the Kingdom of Saudi Arabia (KSA) were analyzed over the 39-year period of 1980-2018. The hourly DI was estimated based on air temperature and relative humidity data obtained from the next-generation global reanalysis from the European Center for Medium-Range Weather Forecasts and in-house high-resolution regional reanalysis generated using an assimilative Weather Research Forecast (WRF) model. The DI exceeds 28°C, that is, the threshold for human discomfort, in all summer months (June to September) over most parts of the KSA due to a combination of consistently high temperatures and relative humidity. The DI is greater than 28°C for 8-16 h over the western parts of KSA and north of the central Red Sea. A DI of >28°C persistes for 7-9 h over the Red Sea and western KSA for 90% of summer days. The spatial extent and number of days with DI > 30°C, that is, the threshold for severe human discomfort, are significantly lower than those with DI > 28°C. Long-term trends in the number of days with DI > 28°C indicate a reduced rate of increase or even a decrease over some parts of the southwestern KSA in recent decades (1999-2018). Areas with DI > 30°C, in particular the northwestern regions of the Arabian Gulf and its adjoining regions, also showed improved comfort levels during recent decades. Significant increases in population and urbanization have been reported throughout the KSA during the study period. Analysis of five-years clinical data suggests a positive correlation between higher temperatures and humidity with heat-related deaths during the Hajj pilgrimage. The information provided herein is expected to aid national authorities and policymakers in developing necessary strategies to mitigate the exposure of humans to high levels of thermal discomfort in the KSA.
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The increase of the temperature in the Red Sea basin due to global warming could have a large negative effect on its marine ecosystem. Consequently, there is a growing interest, from the scientific community and public organizations, in obtaining reliable projections of the Red Sea temperatures throughout the 21st century. However, the main tool used to do climate projections, the global climate models (GCM), may not be well suited for that relatively small region. In this work we assess the skills of the CMIP5 ensemble of GCMs in reproducing different aspects of the Red Sea 3D temperature variability. The results suggest that some of the GCMs are able to reproduce the present variability at large spatial scales with accuracy comparable to medium and high-resolution hindcasts. In general, the skills of the GCMs are better inside the Red Sea than outside, in the Gulf of Aden. Based on their performance, 8 of the original ensemble of 43 GCMs have been selected to project the temperature evolution of the basin. Bearing in mind the GCM limitations, this can be an useful benchmark once the high resolution projections are available. Those models project an averaged warming at the end of the century (2080-2100) of 3.3 ±> 0.6°C and 1.6 ±> 0.4°C at the surface under the scenarios RCP8.5 and RCP4.5, respectively. In the deeper layers the warming is projected to be smaller, reaching 2.2 ±> 0.5°C and 1.5 ±> 0.3°C at 300 m. The projected warming will largely overcome the natural multidecadal variability, which could induce temporary and moderate decrease of the temperatures but not enough to fully counteract it. We have also estimated how the rise of the mean temperature could modify the characteristics of the marine heatwaves in the region. The results show that the average length of the heatwaves would increase ~15 times and the intensity of the heatwaves ~4 times with respect to the present conditions under the scenario RCP8.5 (10 time and 3.6 times, respectively, under scenario RCP4.5).
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Clima , Calentamiento Global , Calor , Modelos Teóricos , Océano ÍndicoRESUMEN
Oil spills at sea pose a serious threat to coastal environments. Identifying oil pollution sources could help to investigate unreported spills, and satellite imagery can be an effective tool for this purpose. We present a Bayesian approach to estimate the source parameters of a spill from contours of oil slicks detected by remotely sensed images. Five parameters of interest are estimated: the 2D coordinates of the source of release, the time and duration of the spill, and the quantity of oil released. Two synthetic experiments of a spill released from a fixed point source are investigated, where a contour is fully observed in the first case, while two contours are partially observed at two different times in the second. In both experiments, the proposed method is able to provide good estimates of the parameters along with a level of confidence reflected by the uncertainties within.
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Contaminación por Petróleo , Teorema de Bayes , Monitoreo del Ambiente , Imágenes SatelitalesRESUMEN
The lockdown measures that were taken to combat the COVID-19 pandemic minimized anthropogenic activities and created natural laboratory conditions for studying air quality. Both observations and WRF-Chem simulations show a 20-50% reduction (compared to pre-lockdown and same period of previous year) in the concentrations of most aerosols and trace gases over Northwest India, the Indo Gangetic Plain (IGP), and the Northeast Indian regions. It is shown that this was mainly due to a 70-80% increase in the height of the boundary layer and the low emissions during lockdown. However, a 60-70% increase in the pollutants levels was observed over Central and South India including the Arabian sea and Bay of Bengal during this period, which is attributed to natural processes. Elevated (dust) aerosol layers are transported from the Middle East and Africa via long-range transport, and a decrease in the wind speed (20-40%) caused these aerosols to stagnate, enhancing the aerosol levels over Central and Southern India. A 40-60% increase in relative humidity further amplified aerosol concentrations. The results of this study suggest that besides emissions, natural processes including background meteorology and dynamics, play a crucial role in the pollution concentrations over the Indian sub-continent.
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Contaminantes Atmosféricos/análisis , Contaminación del Aire/análisis , Monitoreo del Ambiente/métodos , Emisiones de Vehículos/análisis , Aerosoles/análisis , África , Bahías , COVID-19 , Control de Enfermedades Transmisibles , Correlación de Datos , Polvo/análisis , Contaminación Ambiental/análisis , Humanos , India , Meteorología , Medio Oriente , Océanos y Mares , PandemiasRESUMEN
This study investigates the risk from oil spills along the main shipping lane in the Red Sea based upon oil spill model trajectories forced by the outputs of validated high resolution regional met-ocean data. Following the intra-annual variations in the met-ocean conditions, the results are presented by classifying the basin into three regions: northern, central and southern Red Sea. The maximum distance traveled by the slick is presented for 1, 2, 5, 10, 14 and 20 days after the commencement of a spill. Different measures of hazard assessment in terms of the concentration of beached oil alongside the corresponding probability maps are also analyzed. The volume fractions of beached, dispersed and evaporated oil, 20 days after the commencement of a spill are quantified. The Red Sea general circulation is characterized by rich mesoscale eddies, which appear to be the most prevailing dynamics in oil transport in the basin. Two case events are analyzed to closely examine the effects of the mesoscale circulations on the fate of spilled oil. The results of this study provide a comprehensive assessment of oil spill hazards in the Red Sea, stemming its main shipping lane and identifies the areas at high risk that require timely mitigation strategies.
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The southern Red Sea is genetically distinct from the rest of the basin; yet the reasons responsible for this genetic separation remain unclear. Connectivity is a vital process for the exchange of individuals and genes among geographically separated populations, and is necessary for maintaining biodiversity and resilience in coral reef ecosystems. Here, using long-term, high-resolution, 3-D backward particle tracking simulations, we investigate the physical connectivity of coral reefs in the southern Red Sea with neighbouring regions. Overall, the simulation results reveal that the southern Red Sea coral reefs are more physically connected with regions in the Indian Ocean (e.g., the Gulf of Aden) than with the northern part of the basin. The identified connectivity exhibits a distinct monsoon-related seasonality. Though beyond the country boundaries, relatively remote regions of the Indian Ocean may have a substantial impact on the southern Red Sea coral reef regions, and this should be taken into consideration when establishing conservation strategies for these vulnerable biodiversity hot-spots.
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Antozoos/fisiología , Biodiversidad , Simulación por Computador , Arrecifes de Coral , Ecosistema , Animales , Océano Índico , Arabia SauditaRESUMEN
Harmful Algal Blooms (HABs) are of global concern, as their presence is often associated with socio-economic and environmental issues including impacts on public health, aquaculture and fisheries. Therefore, monitoring the occurrence and succession of HABs is fundamental for managing coastal regions around the world. Yet, due to the lack of adequate in situ measurements, the detection of HABs in coastal marine ecosystems remains challenging. Sensors on-board satellite platforms have sampled the Earth synoptically for decades, offering an alternative, cost-effective approach to routinely detect and monitor phytoplankton. The Red Sea, a large marine ecosystem characterised by extensive coral reefs, high levels of biodiversity and endemism, and a growing aquaculture industry, is one such region where knowledge of HABs is limited. Here, using high-resolution satellite remote sensing observations (1km, MODIS-Aqua) and a second-order derivative approach, in conjunction with available in situ datasets, we investigate for the first time the capability of a remote sensing model to detect and monitor HABs in the Red Sea. The model is able to successfully detect and generate maps of HABs associated with different phytoplankton functional types, matching concurrent in situ data remarkably well. We also acknowledge the limitations of using a remote-sensing based approach and show that regardless of a HAB's spatial coverage, the model is only capable of detecting the presence of a HAB when the Chl-a concentrations exceed a minimum value of ~ 1 mg m-3. Despite the difficulties in detecting HABs at lower concentrations, and identifying species toxicity levels (only possible through in situ measurements), the proposed method has the potential to map the reported spatial distribution of several HAB species over the last two decades. Such information is essential for the regional economy (i.e., aquaculture, fisheries & tourism), and will support the management and sustainability of the Red Sea's coastal economic zone.