Management Options to Improve Water Quality in Lake Peipsi: Insights from Large Scale Models and Remote Sensing

Abstract

Nutrient pollution causes frequent blooms of potentially harmful cyanobacteria in Lake Peipsi (Estonia/Russia). Although external nutrient loading has reduced since the 1990s, lake water quality has barely improved, and eutrophication is still considered a threat to lake biota and water usage. To understand the recovery dynamics of the lake it is necessary to analyse the effects of land use and lake management on water quality to develop mitigation strategies. Comprehensive analysis has thus far failed due to information gaps inherent to conventional monitoring strategies. We show how two large-scale hydrological models using Earth observation data provide spatial information on pollution and can help explain the causes of past and current lake eutrophication. WaterGAP3.2 provides valid estimates of present and probable future phosphorus concentration in the lake water, based on past hydrological conditions. WaterWorld models spatial potential water quality and a scenario of optimal pollution reduction. Remotely sensed optical water quality data can be used to analyse recent, spatial water quality dynamics. The spatial and temporal algae distributions and can help explain eutrophication causes at Lake Peipsi and its catchment, adding value to in situ monitoring and supporting river basin management with large scale data.