Laguna Lake Research Articles

Social Capital and Vulnerability to Extreme Climate in a Semi-urban Fishing Community in Laguna de Bay, Philippines

The study looked into the risks associated with extreme climate events in the case of a semi-urban fishing community surrounding Laguna Lake in the Philippines. A survey was undertaken to determine the economic effects (loss of assets, foregone income, and changes in consumption patterns) of strong typhoons and torrential rains on fishing households. Vulnerability, estimated as the perceived probability of lower consumption after flooding or typhoons, was used to assess the economic impact on households. Household characteristics, including social capital, that may influence consumption vulnerability, were analyzed using a binary probit regression model. Social capital, a multi-dimensional concept consisting of social networks and skills possessed and used by household members to facilitate actions, was modeled using four indicators – two associational (membership in a formal organization and usefulness of informal social networks) and two behavioral (trust and cooperativeness). Regression results revealed that fishing income and household size significantly affect vulnerability. The higher the fish catch and the smaller the household, the less vulnerable is the household to strong storms and torrential rains. Social capital indicators do not significantly affect consumption vulnerability of households.

Solid Waste Management in Urban and Rural Communities of Santa Cruz Watershed, Laguna, Philippines

Solid waste management (SWM) has become a continuing hurdle for governments and communities because of its multi-faceted nature. With the worsening SWM problem, the Philippines enacted Republic Act 9003, the Ecological Solid Waste Management (SWM) Act 2000, instituting SWM Program. Water quality in water bodies and watersheds is adversely affected by pollution, exacerbated by increasing population and urbanization. This study examined RA 9003 implementation in urban and rural communities of Santa Cruz Watershed (SCW) which drains into Laguna Lake. Knowledge, awareness, and perceptions (KAPs) of communities were elicited using focused group discussions, interviews, and surveys in barangays within the watershed. Municipalities have completed respective10- year SWM Plans, organized SWM Boards, and SWM committees in villages. Communities showed satisfaction with SWM implementation and monitoring but rural communities had higher overall SWM effectiveness ratings. Both communities were willing to pay for a cleaner environment. Challenges that hamper better implementation of RA 9003 include non-segregation of solid waste by residents attributed to lack of awareness and discipline; irregular garbage collection; inadequate garbage trucks; lack of functional materials recovery facilities; and limited government resources. Addressing these concerns will further boost RA 9003 compliance and enhance the effectiveness of implementation and monitoring in the communities.

Benefits from Laguna Lake: Perspective of Small Fisher Households

Launched in June 2013, the Journal of Management for Global Sustainability (JMGS) is an internationally peer-reviewed open access scholarly journal on management and global sustainability published semi-annually by the International Association of Jesuit Business Schools (IAJBS) and managed by the John Gokongwei School of Management of the Ateneo de Manila University. Printed copies are regularly distributed to more than 300 IAJBS member institutions and top business schools around the globe while the online version is available to all readers worldwide. JMGS is indexed in EconBib and Mir@bel.

Economic Vulnerabilities of Fishing-dependent Households Around Laguna Lake, Philippines

The study characterizes the mainly subsistence fishing communities surrounding Laguna Lake and provides empirical evidence of the economic deprivation that they are experiencing using survey data. Following a multi-dimensional approach, the paper focuses on consumption expenditures as the prime indicator of economic well-being – together with assets ownership, financial behavior, and social engagements. Regression analyses are conducted to identify the factors underlying consumption and to determine how different forms of household capital (physical, financial, and social) and fishing activities affect the capacity to generate income or livelihood. The study finds that: 1) food consumption (mainly rice) accounts for half of the household’s total expenditures, is very income-inelastic, and is mainly determined by household size; 2) all consumption expenditures are income-inelastic and are therefore basic necessities, except for mobile phone load, the only luxury consumption expenditure for these low-income fishing households; (3) while derived income (sum of all expenditures and savings) is not significantly determined by any form of capital, it is significantly higher for households undertaking aquaculture; and (4) the conditional cash transfer of the government significantly contributes to household consumption as an income augmentation measure but does not significantly lower food shortage vulnerability of the household.

Characterization of water quality and fluorescence measurements of dissolved organic matter in Cabuyao river and its tributaries using excitation-emission matrix spectroscopy

Laguna lake is an important natural resource that provides agricultural and other uses such as transportation, recreation, and irrigation. Due to rapid changes in the land use near the west region of Laguna lake, surface water quality measurements are necessary. Using excitation-emission fluorescence spectroscopy, the optical characteristics of dissolved organic matter (DOM) in Cabuyao river were assessed. There are six sampling sites based on inflow and outflow of water from Cabuyao river and its tributaries. 3D excitation-emission matrices (EEMs) using Surfer v16 was utilized in the assessment of protein-like and humic-like fluorescence peaks. It was observed that the differences in the EEMs provided DOM composition, and its origin. A fluorescence index of approximately 1.18 showed that most of the samples are terrestrial in origin. In-situ water quality measurements such as dissolved oxygen, temperature, pH, and electrical conductivity. Hence, this study provides preliminary information on the possible use of fluorescence EEMs in providing DOM composition in natural waters.

Reed Bed System for the Improvement of Laguna Lake Waters

Laguna lake is the biggest lake and considered one of the most important inland bodies of water in the Philippines due to its many uses. However, the lake has faced growing pressures (i.e., population expansion, urbanization, industrialization, deforestation, and land conversion) on its ecosystems over the past decades. These activities have led to the degradation of the lake’s water quality and ecosystem.The Department of Environment and Natural Resources (DENR) provided a water quality standard for freshwater in determining trends and in the evaluation of stages of deterioration or enhancement in quality of water which include 1) Class AA (Public Water Supply Class I); 2) Class A (Public Water Supply Class II); 3) Class B (Recreational Water Class I); Class C (Propagation and growth of fish class); and Class D (Navigable Waters). The classification of Laguna Lake falls under Class C, which means it is suitable for fishery. With this, intervention for the improvement of the water in Laguna Lake is highly needed. This study aims to design and assess the effectiveness of the reed bed system for the improvement of Laguna Lake. Results show that using reed bed water treatment; the total coliform will meet the standards of DENR after three years of treatment, which can be used as a source of water for irrigation, recreational area, and propagation of fishes. The concentration of ammonia will meet the standards of DENR after two and a half years by using reed bed water treatment, and inorganic phosphate will meet the standards of DENR after three years by using reed bed water treatment.

Typhoon Awareness and Flood Management Measures: The Case of Barangay Santo Domingo, Bay, Laguna, Philippines

The Philippines is prone typhoons due to its geographical location. The municipalities of Los Baños and Bay in the province of Laguna are highly flood-prone due to their proximity to Laguna de Bay or Laguna Lake. Large areas of Bay fall within the watersheds of Mt. Makiling Forest Reserve (MMFR) which are tributaries to the lake. The back flow of water into the trubutaries during heavy rainfall can cause flooding and result to socio-economic and physical impacts. The study aims to: determine the association of typhoon awareness, mitigation practices, and resilience practices with the socio-economic and socio-demographic profile of Barangay (village) Santo Domingo residents; generate a Flood Susceptibility Map, map the designated evacuation sites using Geographic Information System (GIS); and provide recommendations to promote awareness and reduce the impacts of typhoon. Ninety-six households were selected randomly based on the sampling design with 95% confidentiality. Key Informant Interviews (KIIs) and Socio-economic surveys (SES) were also conducted. STATA ver. 12 and Fisher’s exact test were used to evaluate the significance of the socio-economic and socio-demographic components in relationship to awareness, mitigation, and resilience. Barangay Santo Domingo has high level of awareness (0.9647), high level of mitigation practices (0.8303) and high level of resilience (0.8842). The monthly income of respondents has a strong correlation with mitigation practices while house ownership signifies moderate association to resilience practices. Chi-square test of independence showed that there is no significant difference among the puroks (zones within the barangay). Based on the generated Flood Susceptibility Map, majority of the land area (197.23 hectares) of Barangay Santo Domingo belongs to the high susceptibility category which led to the identification of six evacuation sites. Awareness is necessary for disaster preparedness. Effective planning and management are essential to increase awareness and reduce the detrimental effects of flooding.

WaterNet: A Convolutional Neural Network for Chlorophyll-a Concentration Retrieval

The retrieval of chlorophyll-a (Chl-a) concentrations relies on empirical or analytical analyses, which generally experience difficulties from the diversity of inland waters in statistical analyses and the complexity of radiative transfer equations in analytical analyses, respectively. Previous studies proposed the utilization of artificial neural networks (ANNs) to alleviate these problems. However, ANNs do not consider the problem of insufficient in situ samples during model training, and they do not fully utilize the spatial and spectral information of remote sensing images in neural networks. In this study, a two-stage training is introduced to address the problem regarding sample insufficiency. The neural network is pretrained using the samples derived from an existing Chl-a concentration model in the first stage, and the pretrained model is refined with in situ samples in the second stage. A novel convolutional neural network for Chl-a concentration retrieval called WaterNet is proposed which utilizes both spectral and spatial information of remote sensing images. In addition, an end-to-end structure that integrates feature extraction, band expansion, and Chl-a estimation into the neural network leads to an efficient and effective Chl-a concentration retrieval. In experiments, Sentinel-3 images with the same acquisition days of in situ measurements over Laguna Lake in the Philippines were used to train and evaluate WaterNet. The quantitative analyses show that the two-stage training is more likely than the one-stage training to reach the global optimum in the optimization, and WaterNet with two-stage training outperforms, in terms of estimation accuracy, related ANN-based and band-combination-based Chl-a concentration models.

Occurrence and distribution of hormones and bisphenol A in Laguna Lake, Philippines

The occurrence of hormones and hormone-like compounds in water has been regarded as an emerging environmental concern. These water contaminants are known to cause endocrine disruption that may affect aquatic organisms. In this study, hormones and phenolic xenoestrogens were investigated and measured in the surface waters of Laguna Lake, Philippines. Laguna Lake is the largest lake in the country that has many uses including fish cultivation and source for potable water production. The hormones estrone, 17-beta-estradiol, 17-alpha-ethynylestradiol, progesterone and testosterone as well as the plasticizer bisphenol A in the lake water were determined by solid phase extraction and LC-MS/MS. The extraction method exhibited good recoveries (75–90% in spiked lake water) while the instrumental method of analysis has low detection limits (0.01–0.24 μg/L) and good linear response (>99% in the 0–50 μg/L concentration range). In the nine sampling sites across the lake, estrone was found in concentrations between 0.03 and 0.30 ng/L. 17-Beta-estradiol, testosterone and progesterone were detected in some of the sites in concentrations up to 0.36, 0.22, and 2.05 ng/L, respectively. Bisphenol A was detected in all sites in higher concentrations of 0.71–47.40 ng/L. Although there are no local guidelines yet, the determination and monitoring of these emerging water contaminants are important because of their potential environmental impacts. Further to this initial study, investigations on point sources spanning the tributary rivers, long-term determination of locational and temporal variations, and assessment of ecotoxicological risks are needed. These are crucial in the regulation and mitigation of discharges into the lake.

Concentrations of Trace Elements in Daily Life Water Consumed in Angono, Rizal, Philippines

Objective. The present study aims to determine the concentrations of seven trace elements [chromium (Cr), arsenic (As), cadmium (Cd), cesium (Cs), lead (Pb), thorium (Th), and uranium (U)] in tap, well, spring, river, and lake waters in the town of Angono, Rizal in the Philippines, consumed for drinking, cooking, bathing, and washing. In the town of Angono, both of the tap and purified tap waters produced in the refilling station are consumed as the water for drinking.Methods. Water samples from six unpurified tap, six purified taps, 10 wells, two springs, two rivers, and one lake water were obtained in October 2015. Samples were analyzed using solution nebulization inductively coupled plasma mass spectrometry. Results were evaluated for health risks of the trace elements in the water samples using WHO guideline values. The provisional WHO guideline values are as follows: Cr: 50 µg/L, As: 10 µg/L, Cd: 3 µg/L, Pb: 10 µg/L, and U: 30 µg/L. That of Cs has not been proposed yet. The WHO guideline value for the naturally occurring radioactive isotope 232Th in drinking water is 1 Bq/L, which is equivalent to 250 μg/L.Results. The concentrations of the trace elements in the tap water samples were <0.5% of the WHO guideline values, except Cs and Cd, with those of Cd being ~9% of the WHO guideline value. Purified tap waters contained the seven trace elements in lower concentrations, with the exception of Pb, which may have been added in small amounts from equipment used for water purification. The concentrations of the trace elements in water samples from the wells and springs in Angono were more variable than those of the tap waters, and these were also below the WHO guideline values. The concentrations of Cs in all water samples were <0.3 µg/L. The trace-element concentrations of the waters from Angono River and Laguna Lake were almost the same as those of the well and spring waters. Conclusion. At present, the concentrations of trace elements in daily life water consumed in Angono are at safe levels.

ESTIMATION OF CHL-A CONCENTRATION IN LAGUNA LAKE USING SENTINEL-3 OLCI IMAGES

Abstract. The use of Sentinel-3 Ocean and Land Color Instrument (OLCI) images in estimating chlorophyll-a (total and class-differentiated)a concentration is promising owing to Sentinel-3’s 21 bands. This was investigated for the case of Laguna de Bay (or Laguna Lake), Philippines. Field surveys were conducted on 13–17 November 2018 using FluoroProbe, a submersible fluorimeter capable of quantifying concentrations of spectral classes of microalgae. These were regressed with reflectance data obtained from 10-day composite Sentinel-3 reflectance images as well as ten empirical algorithms (indices) for OLCI. Compared to band reflectance, the 10 indices yielded stronger correlations, especially with R665/R709, R674/R709, and (1/R665-1/R709)xR754 with the following respective correlation values: −0.623, −0.646, and 0.628. Multiple regression results indicates that 48% of the variability of total chl-a concentration is explained by five explanatory (reflectance) variables (R412, R443, R560, R681, and R754) with RMSE of 2.814 μg/l. In contrast, the two indices R674/R754 and (1/R665-1/R709)xR754 accounted for about 46% of the variability of total chl-a concentration with RMSE of 2.475 μg/l. For diatoms and bluegreen microalgae, R560/R665 and (1/R665-1/R709)xR754 constitute the models with R2 of 0.21 and 0.435, and RMSE of 2.516 and 2.163 ug/l, respectively. Green microalgal concentration is jointly described by three indices: R560/R665, R674/R754, and R709-R754, with R2 = 0.182 and RMSE = 1.219 μg/l. From cryptophytes, the model comprising of R560/R665, (1/R665-1/R709)xR754, and R709-R754 produced an R2 = 0.289 and RMSE = 0.767 μg/l. It can be said that the empirical algorithms can be used for Sentinel-3 OLCI data providing acceptable estimations of total and spectral class-differentiated chl-a concentration.

OPTMIZATION OF BIO-OPTICAL MODEL PARAMETERS FOR TURBID LAKE WATER QUALITY ESTIMATION USING LANDSAT 8 AND WASI-2D

Abstract. Laguna Lake, the Philippines’ largest freshwater lake, has always been historically, economically, and ecologically significant to the people living near it. However, as it lies at the center of urban development in Metro Manila, it suffers from water quality degradation. Water quality sampling by current field methods is not enough to assess the spatial and temporal variations of water quality in the lake. Regular water quality monitoring is advised, and remote sensing addresses the need for a synchronized and frequent observation and provides an efficient way to obtain bio-optical water quality parameters. Optimization of bio-optical models is done as local parameters change regionally and seasonally, thus requiring calibration. Field spectral measurements and in-situ water quality data taken during simultaneous satellite overpass were used to calibrate the bio-optical modelling tool WASI-2D to get estimates of chlorophyll-a concentration from the corresponding Landsat-8 images. The initial output values for chlorophyll-a concentration, which ranges from 10–40 μg/L, has an RMSE of up to 10 μg/L when compared with in situ data. Further refinements in the initial and constant parameters of the model resulted in an improved chlorophyll-a concentration retrieval from the Landsat-8 images. The outputs provided a chlorophyll-a concentration range from 5–12 μg/L, well within the usual range of measured values in the lake, with an RMSE of 2.28 μg/L compared to in situ data.

LAND COVER MONITORING OF LAGUNA LAKE WATERSHED USING MODIS NDVI DATA

Abstract. Land use and land cover monitoring is an important component in the management of Laguna Lake watershed due to its impacts on the lake’s water quality. Due to limitations caused by cloud cover, satellite systems with limited revisit capability fail to provide sufficient data to more effectively monitor the land surface. Normalized difference vegetation index (NDVI) derived from MODIS image data were used to generate land cover maps for the years 2001, 2005, 2009, 2013, and 2017. These were produced by classifying ISODATA classes using annual NDVI profiles, which resulted in land cover classes, namely, agricultural land, built-up, forest, rangeland, water, and wetland. The resulting maps were post-processed using multi-variate alteration detection (MAD), resulting in multi-temporal land cover maps with improved overall accuracies and kappa coefficients that indicate moderate agreement with ground truth data. Spatiotemporal hot spot analysis was also performed using NDVI data from 2001 to 2017 to identify vegetation hot spot areas, where clustering of low NDVI values were observed over the years. Results showed an increasing trend in built-up areas accompanied by decreasing trends in water and wetland areas, indicating impacts caused by land reclamation and expansion of residential subdivisions near the lakeshore. The decrease in total vegetation area from 2001 to 2017 could be attributed to conversion of land to built-up surface. Vegetated areas in identified hot spots decreased from 41% in 2001 to 19% in 2017. This suggests that vegetation cover in these hot spots was converted to non-vegetated surface during the time period studied.

Computational and Numerical Modeling for Classification of Water Quality of Lake

In this study, the Multivariate Linear Regression (MLR), Artificial Neural Network (ANN), k-Nearest Neighbour (kNN), and Support Vector Machine (SVM) models had been developed to simulate and to predict the water quality of Laguna Lake. The input variables for the MLR model had been determined through linear regression. The ANN, kNN, and SVM had been modelled per water quality parameter with cross validation and evaluated through its accuracy. The performance of the MLR models had been evaluated with the statistical metrics R-squared, Mean Absolute Error, and Root Mean Square Error. A web-based water quality monitoring had been developed to incorporate in their monitoring. The results had indicated that the performance of SVM is superior in the prediction of classes in most water quality parameters. The study results had shown that the poor correlation between the water quality parameters indicated that the data cannot be modelled. The results had shown that the correlation had not reached the threshold to be significant of 60% for R-squared. As per the classification models, the results of the comparison had shown that SVM had been the best model in the majority of parameters.

The Administrative Performance of the Laguna Lake Development Authority on the Small Lakes of the Laguna de Bay Region, Philippines

The Administrative Performance of the Laguna Lake Development Authority on the Small Lakes of the Laguna de Bay Region, Philippines

Selecting rep-PCR markers to source track fecal contamination in Laguna Lake, Philippines.

Fecal contamination is one of the factors causing deterioration of Laguna Lake. Although total coliform levels are constantly monitored, no protocol is in place to identify their origin. This can be addressed using the library-dependent microbial source tracking (MST) method, repetitive element sequence-based polymerase chain reaction (rep-PCR) fingerprinting. Serving as a prerequisite in developing the host-origin library, we assessed the discriminatory power of three fingerprinting primers, namely BOX-A1R, (GTG)5, and REP1R-1/2-1. Fingerprint profiles were obtained from 290 thermotolerant Escherichia coli isolated from sewage waters and fecal samples of cows, chickens, and pigs from regions surrounding the lake. Band patterns were converted into binary profiles and were classified using the discriminant analysis of principal components. Results show that: (1) REP1R-1/2-1 has a low genotyping success rate and information content; (2) increasing the library size led to more precise estimates of library accuracy; and (3) combining fingerprint profiles from BOX-A1R and (GTG)5 revealed the best discrimination (average rate of correct classification (ARCC) = 0.82 ± 0.06) in a two-way categorical split; while (4) no significant difference was found between the combined profiles (0.74 ± 0.15) and using solely BOX-A1R (0.76 ± 0.09) in a four-way split. Testing the library by identifying known isolates from a separate dataset has shown that a two-way classification performed better (ARCC = 0.66) than a four-way split (ARCC = 0.29). The library can be developed further by adding more representative isolates per host source. Nevertheless, our results have shown that combining profiles from BOX-A1R and (GTG)5 is recommended in developing the MST library for Laguna Lake.

ESTIMATION OF CHLOROPHYLL-A CONCENTRATION IN LAGUNA DE BAY USING SENTINEL-3 SATELLITE DATA

Abstract. The mission of the European Space Agency’s Sentinel-3 satellite is to provide data for land and ocean monitoring purposes. Sentinel- 3’s Ocean and Land Colour Instrument (OLCI) data are being used largely for monitoring offshore and coastal waters but can also be used for inland waters including lakes. It has a spatial resolution of 300 meters, a temporal resolution of 2–3 days and contains 21 spectral bands. Laguna de Bay, with a surface area of around 930 km2, suffers from periodic algal blooms resulting from excessive nutrient inputs from surrounding watersheds. This study aims to assess the applicability of Sentinel-3 OLCI for estimating chlorophylla (chl-a) concentration in Laguna Lake. Several chl-a estimation algorithms (i.e., band ratios and indices) were tested for Sentinel 3 OLCI images and compared with in-situ data obtained using a chl-a sensor. A regression model comprising of individual spectral bands, band ratios, and band indices for chl-a estimation as independent variables was developed, yielding an adjusted R2 of 0.759 and RMSE of 1.19 ug/L. The model consists of R620, R674/R708, and RED/NIR. A map was produced showing the spatial distribution of chl-a in Laguna Lake, with most of the portion of the lake having a concentration ranging from 7.5 ug/L to 15 ug/L. This shows that Sentinel 3 OLCI images can be utilized for accurately estimating chl-a in Laguna Lake.

RADIOMETRIC CALIBRATION OF LANDSAT-8 OLI AND SENTINEL-2 MSI IMAGES FOR WATER QUALITY MODELLING OF LAGUNA LAKE, PHILIPPINES

Abstract. Regular monitoring of water quality in Laguna Lake is important for it supports aquaculture and provides water supply for Metro Manila. Remote sensing makes it possible to monitor the spectral conditions of the lake on a regular time interval and with complete coverage except for the areas with cloud and shadow cover. Along with in-situ water quality measurements, bio-optical models can be developed to determine the relationship between spectral and bio-optical properties of the lake water and consequently enables the estimation of water quality through remote sensing. However, radiometric calibration is needed to minimize the effects of the changing atmospheric conditions over time and to account for the difference in sensors (e.g., Landsat-8 OLI, Sentinel-2 MSI) used for water quality assessment. Canonical correlation analysis is used to detect pseudo-invariant features (PIFs), which are ground objects that do not dramatically vary in spectral properties over time. Road surface and other large man-made infrastructures are the commonly detected PIFs. These PIFs are used to compute for the parameters used to normalize reflectance values of remotely-sensed images obtained on different dates and using different sensors. The normalization resulted to a reduction of difference in reflectance values between the reference image and the adjusted image, though not marginal. This is due to the use of a linear equation to adjust the image, which limits the ability of the reflectance values of the image to fit to the values of the reference image.

CHLOROPHYLL-A CONCENTRATION RETRIEVAL USING CONVOLUTIONAL NEURAL NETWORKS IN LAGUNA LAKE, PHILIPPINES

Abstract. Two existing chlorophyll-a (chl-a) concentration retrieval procedures, which are analytical and empirical, are hindered by the complexity in radiative transfer equation (RTE) and in statistical analyses, respectively. Another promising model in this direction is the use of artificial neural networks (ANN). Mostly, a pixel-to-pixel with one-layer ANN model is used; where in fact that the satellite instrumental errors and man-made objects in water bodies might affect the retrieval and should be taken into account. In this study, the mask-based neural structure, called convolutional neural networks (CNN) model containing both the target and neighborhood pixels, is proposed to reduce the influence of the aforementioned premises. The proposed model is an end-to-end multiple-layer model which integrates band expansion, feature extraction, and chl-a estimation into the structure, leading to an optimal chl-a concentration retrieval. In addition to that, a two-stage training is also proposed to solve the problem of insufficient in-situ samples which happens in most of the time. In the first stage, the proposed model is trained by using the chl-a concentration derived from the water product, provided by satellite agency, and is refined with the in-situ samples in the second stage. Eight Sentinel-3 images from different acquisition time and coincide in-situ measurements over Laguna Lake waters of Philippines were utilized to conduct the model training and testing. Based on quantitative accuracy assessment, the proposed method outperformed the existing dual- and triple- bands combinations in chl-a concentration retrieval.

BIO OPTICAL MODELLING OF LAGUNA LAKE USING BOMBER TOOL AND WASI-DERIVED INVERTED PARAMETERS

Abstract. Laguna Lake is significant to its surrounding cities and municipalities as it serves multiple purposes: flood basin, aquaculture, water source for irrigation and domestic use, among others. Monitoring the lake’s water quality is an integral part ensuring that the lake would continue to serve its purposes. Bio-optical modelling is a type of empirical model that relates the inherent optical properties of water to different biological properties like chlorophyll-a. The BOMBER (Bio-Optical Model Based tool for Estimating water quality and bottom properties from Remote sensing images) tool makes use of the different IOPs apparent optical properties (AOPs) of satellite images to be able to produce water quality maps. To localize the parameters used by the BOMBER tool, the use of WASI (The Water Color Simulator) tool was introduced. Inverting in situ spectral measurements of the lake, WASI tool was able to produce parameters localized for the lake. This research used 2018 Landsat 8 Images to produce images and used a water profiler to validate results. Results show the bio-optical model provided a R-squared value of 0.6912 and an RMSE of 2.43 μg/l which shows good correlation between the in-situ and the bio-optical model results.