Microcystins (MCs) and cylindrospermopsin (CYN) are the most abundant toxins produced by cyanobacteria in tropical freshwaters. We studied the spatial distribution of MC and CYN in two multipurpose reservoirs, Mahakanadarawa and Nachchaduwa in Anuradhapura district in Sri Lanka in September 2020. Fourteen water quality parameters, phytoplankton composition, chlorophyll-a, MC and CYN concentrations were analyzed in triplicate in 25 sampling sites from each reservoir. Both reservoirs were at hypereutrophic status. Microcystis was the dominant cyanobacteria with 0-3.75 x 10 3 cell/mL in Mahakanadarawa and 1-7 x 10 3 cell/mL in Nachchaduwa. Besides Microcystis, no other potential MCproducing cyanobacteria were observed. In Mahakanadarawa, MC was detected in the range of 0.11-1.63 µg/L which was above the WHO permissible level (1.0 µg/L) for drinking water. Although comparatively high Microcystis cell density was present in Nachchaduwa, its MC concentration was low (0.06-0.17 µg/L). The CYN concentration in Nachchaduwa was above the WHO permissible level (0.7 µg/L) for drinking water. It was 0.20-1.02 µg/L in Nachchaduwa and 0.03-0.08 µg/L in Mahakanadarawa. We did not observe any potential CYN-producing cyanobacteria in either of the reservoirs. There was no relationship between the spatial distribution pattern of MC and Microcystis cell density in both reservoirs. Although the majority of physico-chemical properties of water indicated suitability for drinking, co-occurrence of high concentrations of MC and CYN indicated their unsuitability for drinking. Hence, this study highlights the necessity for routing detection of cyanotoxins in both reservoirs. Further, our findings alarm potential health risks for the local community that relies on Mahakanadarawa and Nachchaduwa reservoirs for drinking, irrigation and fisheries.

This study aimed to develop an empirical model to predict the spatial distribution of Aphanizomenon using the Ridiyagama reservoir in Sri Lanka with a dual-model strategy. In December 2020, a bloom was detected with a high density of Aphanizomenon and chlorophyll-a concentration. We generated a set of algorithms using in situ chlorophyll-a data with surface reflectance of Sentinel-2 bands on the same day using linear regression analysis. The in situ chlorophyll-a concentration was better regressed to the reflectance ratio of (1+R665)/(1–R705) derived from B4 and B5 bands of Sentinel-2 with high reliability (R2=0.81, p<0.001). The second regression model was developed to predict Aphanizomenon cell density using chlorophyll-a as the proxy and the relationship was strong and significant (R2=0.75, p<0.001). Coupling the former regression models, an empirical model was derived to predict Aphanizomenon cell density in the same reservoir with high reliability (R2=0.71, p<0.001). Furthermore, the predicted and observed spatial distribution of Aphanizomenon was fairly agreed. Our results highlight that the present empirical model has a high capability for an accurate prediction of Aphanizomenon cell density and their spatial distribution in freshwaters, which helps in the management of toxic algal blooms and associated health impacts.