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Excessive algae growth can lead to negative consequences for ecosystem function, economic opportunity, and human and animal health. Due to the cost‐effectiveness and temporal availability of satellite imagery, remote sensing has become a powerful tool for water quality monitoring. The use of remotely sensed products to monitor water quality related to algae and cyanobacteria productivity during a bloom event may help inform management strategies for inland waters. To evaluate the ability of satellite imagery to monitor algae pigments and dissolved oxygen conditions in a small inland lake, chlorophyll‐a, phycocyanin, and dissolved oxygen concentrations are measured using a YSI EXO2 sonde during Sentinel‐2 and Sentinel‐3 overpasses from 2019 to 2022 on Lake Mendota, WI. Machine learning methods are implemented with existing algorithms to model chlorophyll‐a, phycocyanin, and Pc:Chla. A novel machine learning‐based dissolved oxygen modeling approach is developed using algae pigment concentrations as predictors. Best model results based on Sentinel‐2 (Sentinel‐3) imagery achieved R²scores of 0.47 (0.42) for chlorophyll‐a, 0.69 (0.22) for phycocyanin, and 0.70 (0.41) for Pc:Chla. Dissolved oxygen models achieved anR²of 0.68 (0.36) when applied to Sentinel‐2 (Sentinel‐3) imagery, and Pc:Chla is found to be the most important predictive feature. Random forest models are better suited to water quality estimations in this system given built in methods for feature selection and a relatively small data set. Use of these approaches for estimation of Pc:Chla and dissolved oxygen can increase the water quality information extracted from satellite imagery and improve characterization of algae conditions among inland waters.

 

This data was collected over 34 sampling trips during four summers (May-October), 2019-2022. 35 grid boxes were generated over Lake Mendota. Before each sampling effort, sample point locations were randomized within each grid box. Surface measurements were taken with an EXO multi-parameter sonde at a subset of the 35 grid boxes throughout Lake Mendota during each sampling trip. Measurements include temperature, conductivity, chlorophyll, phycocyanin, turbidity, dissolved organic material, ODO, pH, and pressure. 

As anthropogenic eutrophication and the associated increase of cyanobacteria continue to plague inland waterbodies, local officials are seeking novel methods to proactively manage water resources. Cyanobacteria are of particular concern to health officials due to their ability to produce dangerous hepatotoxins and neurotoxins, which can threaten waterbodies for recreational and drinking-water purposes. Presently, however, there is no cyanobacteria outlook that can provide advance warning of a potential threat at the seasonal time scale. In this study, a statistical model is developed utilizing local and global scale season-ahead hydroclimatic predictors to evaluate the potential for informative cyanobacteria biomass and associated beach closure forecasts across the June–August season for a eutrophic lake in Wisconsin (United States). This model is developed as part of a subseasonal to seasonal cyanobacteria forecasting system to optimize lake management across the peak cyanobacteria season. Model skill is significant in comparison to June–August cyanobacteria observations (Pearson correlation coefficient = 0.62, Heidke skill score = 0.38). The modeling framework proposed here demonstrates encouraging prediction skill and offers the possibility of advanced beach management applications. 

Although scientists agree that climate change is anthropogenic, differing interpretations of evidence in a highly polarized sociopolitical environment impact how individuals perceive climate change. While prior work suggests that individuals experience climate change through local conditions, there is a lack of consensus on how personal experience with extreme precipitation may alter public opinion on climate change. We combine high-resolution precipitation data at the zip-code level with nationally representative public opinion survey results (n= 4008) that examine beliefs in climate change and the perceived cause. Our findings support relationships between well-established value systems (i.e., partisanship, religion) and socioeconomic status with individual opinions of climate change, showing that these values are influential in opinion formation on climate issues. We also show that experiencing characteristics of atypical precipitation (e.g., more variability than normal, increasing or decreasing trends, or highly recurring extreme events) in a local area are associated with increased belief in anthropogenic climate change. This suggests that individuals in communities that experience greater atypical precipitation may be more accepting of messaging and policy strategies directly aimed at addressing climate change challenges. Thus, communication strategies that leverage individual perception of atypical precipitation at the local level may help tap into certain “experiential” processing methods, making climate change feel less distant. These strategies may help reduce polarization and motivate mitigation and adaptation actions.

Public acceptance for anthropogenic climate change is hindered by how related issues are presented, diverse value systems, and information-processing biases. Personal experiences with extreme weather may act as a salient cue that impacts individuals’ perceptions of climate change. We couple a large, nationally representative public opinion dataset with station precipitation data at the zip-code level in the United States. Results are nuanced but suggest that anomalous and variable precipitation in a local area may be interpreted as evidence for anthropogenic climate change. So, relating atypical local precipitation conditions to climate change may help tap into individuals’ experiential processing, sidestep polarization, and tailor communications at the local level.

 

For countries dependent on rainfed agriculture, failure of the rainy season can lead to serious consequences on the broader economy. Maize, a common staple crop in these countries, often expresses significant interannual variability, given its high sensitivity to water stress. It is traditionally planted at rainy season onset to maximize the growing season and potential yield; however, this risks planting during a ‘false onset’ that can damage the crop or require replanting. Rainy season onset forecasts offer some promise in reducing this risk; however, the potential for increasing yield has not been explicitly quantified. This study quantifies the yield gap associated with suboptimal maize planting times using a process-based crop model over a 36 year historical period across Ethiopia. Onset-informed and forecast-informed approaches are compared with a baseline approach, and results indicate a strong potential for yield gains in drier regions as well as reductions in interannual variance countrywide. In contrast, regions with reliably sufficient precipitation illustrate only minimal gains. In general, integration of onset forecasts into agricultural decision-making warrants inclusion in agricultural extension efforts.