PGIS as a Tool for Reservoir Health Assessment: Community Insights Validated by Laboratory Analysis and Remote Sensing

Abstract

This study employed an innovative participatory geographic information system (PGIS) approach to evaluate the health of reservoirs and their socioecological importance to communities within the Shanzai sub-catchment. The participation rate was 100% in all five communities, with 53% of participants were women. Statiscial analysis shows that algal bloom negatively correlate with less fish productivity and positively with unhealthy reservoir indicators. In contrast, clean water positively correlates with healthy reservoir indicators, while algal blooms consistently show negative correlations with all indicators of healthy reservoir. These findings show current reservoir health, with 41% of participants recognizing reservoirs as healthy, 23% as unhealthy and 36% responded as moderate. Laboratory analysis identified 30 phytoplankton genera, with Cyanophyta as the dominant group. Highest phytoplankton density was observed in May, followed by June and April, providing crucial insights into the seasonal dynamics of reservoir ecosystems. Sentinel-2 imagery further highlighted algal bloom fluctuations, the bloom extent particularly increased during May 2023, supporting water quality measurements and validating algae as a community-identified indicator. This study underlines the value and accuracy of community-driven insights in environmental monitoring. The alignment of participatory mapping, laboratory analyses, and remote sensing demonstrates the efficiency of PGIS in managing freshwater resources. By fostering knowledge exchange, this approach promotes sustainable resource monitoring and conservation. These findings represent a significant contribution to the advancement of reservoir monitoring and underscore the importance of prioritizing community-driven initiatives in environmental research.