Operational Forecasting in Ecology by Inferential Models and Remote Sensing

Abstract

This chapter addresses the demand of environmental agencies and water industries for tools enabling them to prevent and mitigate events of rapid deterioration of environmental assets such as contamination of air, soils and water, declining biodiversity, desertification of landscapes. Getting access to reliable early warning signals may avoid excessive ecological and economic costs.

Here we present examples of recently emerging technologies for predictive modelling and remote sensing suitable for early warning of outbreaks of toxic cyanobacteria blooms in freshwaters that pose a serious threat to public health and biodiversity. As demonstrated by two case studies, inferential models developed from in situ water quality data by evolutionary computation prove to be suitable for up to 30 days forecasting of population dynamics of cyanobacteria and concentrations of cyanotoxins in drinking water reservoirs with different climates. The models not only forecast daily concentrations of cyanobacteria and cyanotoxins but also daily proliferation rates. Proliferation rates exceeding 0.2 day−1 serve as criteria for early warning. Alarm is triggered if forecasted concentrations of cyanobacteria or cyanotoxins exceed predefined threshold values and proliferation rates exceed 0.2 day−1, constituting a bloom event. Findings from these case studies suggest that cyanobacteria blooms can be forecasted up to 30 days ahead in real-time mode solely based on online water quality data monitored by multi-sensor data loggers.

Advanced remote sensing technology allows to quantify absorption/reflectance characteristics of algal pigments of a water column for deriving chlorophyll-a concentrations as indicator for algal biomass, or discriminating cyanobacteria by specific pigments such as cyano-phycocyanin and cyano-phycoerithrin. It has the potential to monitor spatio-temporal distribution of water quality parameters and cyanobacteria blooms based on sufficient spatial, temporal and spectral resolution of the sensors, and the availability of suitable algorithms to match satellite information with high-resolution in-situ measurements. The chapter discusses the prospect of using remote sensing technology for forecasting seasonal trajectories of cyanobacteria blooms that requires the combination of in-situ monitoring and remote sensing data with hydrodynamic models. By deriving vertical light attenuation in the water column from remote sensing data, hydrodynamic models will be enabled to predict seasonally occurring cyanobacteria blooms.