A disposition of interpolation techniques

A large collection of interpolation techniques is available for application in environmental research. To help environmental scientists in choosing an appropriate technique a disposition is made, based on 1) applicability in space, time and space-time, 2) quantification of accuracy of interpolated values, 3) incorporation of ancillary information, and 4) incorporation of process knowledge. The described methods include inverse distance weighting, nearest neighbour methods, geostatistical interpolation methods, Kalman filter methods, Bayesian Maximum Entropy methods, etc. The applicability of methods in aggregation (upscaling) and disaggregation (downscaling) is discussed. Software for interpolation is described. The application of interpolation techniques is illustrated in two case studies: temporal interpolation of indicators for ecological water quality, and spatio-temporal interpolation and aggregation of pesticide concentrations in Dutch surface waters. A valuable next step will be to construct a decision tree or decision support system, that guides the environmental scientist to easy-to-use software implementations that are appropriate to solve their interpolation problem. Validation studies are needed to assess the quality of interpolated values, and the quality of information on uncertainty provided by the interpolation method

Forecasting Harmful Algal Blooms for Western Lake Erie Using Data Driven Machine Learning Techniques

Harmful algal blooms (HAB) have been documented for more than a century occurring all over the world. The western Lake Erie has suffered from Cyanobacteria blooms for many decades. There are currently two widely available HAB forecasting models for Lake Erie. The first forecasting model gives yearly peak bloom forecast while the second provides weekly short-term forecasting and offers size as well as location. This study focuses on bridging the gap of these two models and improve HAB forecast accuracy in western Lake Erie by letting historical observations tell the behavior of HABs. This study tests two machine learning techniques, artificial neural network (ANN) and classification and regression tree (CART), to forecast monthly HAB indicators in western Lake Erie for July to October. ANN and CART models were created with two methods of selecting input variables and two training periods: 2002 to 2011 and 2002 to 2013. First a nutrient loading period method which considers all nutrient contributing variables averaged from March to June and second a Spearman rank correlation to choose separate input sets for each month considering 224 different combinations of averaging and lag periods. The ANN models showed a correlation coefficient increase from 0.70 to 0.77 for the loading method and 0.79 to 0.83 for the Spearman method when extending the training period. The CART models followed a similar trend increasing overall precision from 85.5% to 92.9% for the loading method and 82.1% to 91% for the Spearman method. Both selection methods had similar variable importance with river discharge and phosphorus mass showing high importance across all methods. The major limitation for ANN is the time required for each forecast to be complete while the CART forecasts earlier is only able to produce a class forecast. In future work, the ANN model accuracy can be improved and use new sets of variables to allow earlier HAB forecasts. The final form of ANN and CART models will be coded in a user interface system to forecast HABs. The monthly forecasting system developed allows watershed planners and decision-makers to timely manage HABs in western Lake Erie

Accounting for Critical Attributes and Uncertainty in Flow-Ecology Relationships

Environmental flows are used to maintain streamflow for aquatic species in rivers while also sustaining human water requirements. While there are many approaches to develop environmental flows, they all rely on a strong conceptual understanding of flow-ecology relationships, which are often uncertain. Uncertainty in flow-ecology relationships can stem from using limited data to develop or test relationships or an incomplete understanding of the attributes inherent to each relationship, such as climate and land conditions. Accounting for these attributes and uncertainty in flow-ecology relationships is critical given mounting interest to develop and implement environmental flows at large scales, often with limited information. Using the South Fork Eel River watershed in northern California USA as a case study, I explored attributes and uncertainty in flow-ecology relationships through a targeted review of academic journal articles and Bayesian Network modeling. I found that few relationships describe explicit links between the flow regime and species or cover the full range of climate and land conditions present in the watershed. These gaps informed several scenarios within a Bayesian Network model—represented as different sets of probabilities—which show that model results can differ by up to 50% depending on the uncertainty scenario. This study informs future field monitoring efforts to develop flow-ecology relationships and promotes effective translation and modeling of existing flow-ecology relationships and their uncertainties