Participatory operations model for cost-efficient monitoring and modeling of river basins--A systematic approach

Strategies for integrating scientific evidence in water policy and law in the face of uncertainty

Understanding how human actions and environmental change affect water resources is crucial for addressing complex water management issues. The scientific tools that can produce the necessary information are ecological indicators, referring to measurable properties of the ecosystem state; environmental monitoring, the data collection process that is required to evaluate the progress towards reaching water management goals; mathematical models, linking human disturbances with the ecosystem state to predict environmental impacts; and scenarios, assisting in long-term management and policy implementation. Paradoxically, despite the rapid generation of data, evolving scientific understanding, and recent advancements in systems modeling, there is a striking imbalance between knowledge production and knowledge utilization in decision-making. In this paper, we examine the role and potential capacity of scientific tools in guiding governmental decision-making processes and identify the most critical disparities between water management, policy, law, and science. We demonstrate how the complex, uncertain, and gradually evolving nature of scientific knowledge might not always fit aptly to the legislative and policy processes and structures. We contend that the solution towards increased understanding of socio-ecological systems and reduced uncertainty lies in strengthening the connections between water management theory and practice, among the scientific tools themselves, among different stakeholders, and among the social, economic, and ecological facets of water quality management, law, and policy. We conclude by tying in three knowledge-exchange strategies, namely - adaptive management, Driver-Pressure-Status-Impact-Response (DPSIR) framework, and participatory modeling - that offer complementary perspectives to bridge the gap between science and policy.

Spatiotemporal analysis of lake chlorophyll-a with combined in situ and satellite data

We estimated chlorophyll-a (Chl-a) concentration using various combinations of routine sampling, automatic station measurements, and MERIS satellite images. Our study site was the northern part of the large, shallow, mesotrophic Lake Pyhäjärvi located in southwestern Finland. Various combinations of measurements were interpolated spatiotemporally using a data fusion system (DFS) based on an ensemble Kalman filter and smoother algorithms. The estimated concentrations together with corresponding 68% confidence intervals are presented as time series at routine sampling and automated stations, as maps and as mean values over the EU Water Framework Directive monitoring period, to evaluate the efficiency of various monitoring methods. The mean Chl-a calculated with DFS in June-September was 6.5-7.5 µg/l, depending on the observations used as input. At the routine monitoring station where grab samples were used, the average uncertainty (standard deviation, SD) decreased from 2.7 to 1.6 µg/l when EO data were also included in the estimation. At the automatic station, located 0.9 km from the routine monitoring site, the SD was 0.7 µg/l. The SD of spatial mean concentration decreased from 6.7 to 2.9 µg/l when satellite observations were included in June-September, in addition to in situ monitoring data. This demonstrates the high value of the information derived from satellite observations. The conclusion is that the confidence of Chl-a monitoring could be increased by deploying spatially extensive measurements in the form of satellite imaging or transects conducted with flow-through sensors installed on a boat and spatiotemporal interpolation of the multisource data.

Spatial Variability and Detection Levels for Chlorophyll-a Estimates in High Latitude Lakes Using Landsat Imagery

Monitoring lakes in high-latitude areas can provide a better understanding of freshwater systems sensitivity and accrete knowledge on climate change impacts. Phytoplankton are sensitive to various conditions: warmer temperatures, earlier ice-melt and changing nutrient sources. While satellite imagery can monitor phytoplankton biomass using chlorophyll a (Chl) as a proxy over large areas, detection of Chl in small lakes is hindered by the low spatial resolution of conventional ocean color satellites. The short time-series of the newest generation of space-borne sensors (e.g., Sentinel-2) is a bottleneck for assessing long-term trends. Although previous studies have evaluated the use of high-resolution sensors for assessing lakes’ Chl, it is still unclear how the spatial and temporal variability of Chl concentration affect the performance of satellite estimates. We discuss the suitability of Landsat (LT) 30 m resolution imagery to assess lakes’ Chl concentrations under varying trophic conditions, across extensive high-latitude areas in Finland. We use in situ data obtained from field campaigns in 19 lakes and generate remote sensing estimates of Chl, taking advantage of the long-time span of the LT-5 and LT-7 archives, from 1984 to 2017. Our results show that linear models based on LT data can explain approximately 50% of the Chl interannual variability. However, we demonstrate that the accuracy of the estimates is dependent on the lake’s trophic state, with models performing in average twice as better in lakes with higher Chl concentration (>20 µg/L) in comparison with less eutrophic lakes. Finally, we demonstrate that linear models based on LT data can achieve high accuracy (R2 = 0.9; p-value < 0.05) in determining lakes’ mean Chl concentration, allowing the mapping of the trophic state of lakes across large regions. Given the long time-series and high spatial resolution, LT-based estimates of Chl provide a tool for assessing the impacts of environmental change.

Probabilistic Evaluation of Ecological and Economic Objectives of River Basin Management Reveals a Potential Flaw in the Goal Setting of the EU Water Framework Directive

The biological status of European lakes has not improved as expected despite up-to-date legislation and ecological standards. As a result, the realism of objectives and the attainment of related ecological standards are under doubt. This paper gets to the bottom of a river basin management plan of a eutrophic lake in Finland and presents the ecological and economic impacts of environmental and societal drivers and planned management measures. For these purposes, we performed a Monte Carlo simulation of a diffuse nutrient load, lake water quality and cost-benefit models. Simulations were integrated into a Bayesian influence diagram that revealed the basic uncertainties. It turned out that the attainment of good ecological status as qualified in the Water Framework Directive of the European Union is unlikely within given socio-economic constraints. Therefore, management objectives and ecological and economic standards need to be reassessed and reset to provide a realistic goal setting for management. More effort should be put into the evaluation of the total monetary benefits and on the monitoring of lake phosphorus balances to reduce the uncertainties, and the resulting margin of safety and costs and risks of planned management measures.