Citizen science shows systematic changes in the temperature difference between air and inland waters with global warming

The Potential Role of School Citizen Science Programs in Infectious Disease Surveillance: A Critical Review

Public involvement in science has allowed researchers to collect large-scale and real-time data and also engage citizens, so researchers are adopting citizen science (CS) in many areas. One promising appeal is student participation in CS school programs. In this literature review, we aimed to investigate which school CS programs exist in the areas of (applied) life sciences and if any projects target infectious disease surveillance. This review’s objectives are to determine success factors in terms of data quality and student engagement. After a comprehensive search in biomedical and social databases, we found 23 projects. None of the projects found focused on infectious disease surveillance, and the majority centered around species biodiversity. While a few projects had issues with data quality, simplifying the protocol or allowing students to resubmit data made the data collected more usable. Overall, students at different educational levels and disciplines were able to collect usable data that was comparable to expert data and had positive learning experiences. In this review, we have identified limitations and gaps in reported CS school projects and provided recommendations for establishing future programs. This review shows the value of using CS in collaboration with traditional research techniques to advance future science and increasingly engage communities.

Thermal renaturation of rivers in the post-industrial age - An example of the Przemsza River basin (Poland)

This article concerns temperature, which is one of the most important physical properties of surface waters. During the period of climate warming, tendencies of increasing river temperature have been repeatedly identified in the literature. This article discusses the lowering of the river temperature, a phenomenon rarely considered, which is occurring in a heavily industrialised area in the southern part of Poland (Upper Silesia Region), undergoing deep restructuring. The main aim of the study is to analyse and evaluate the unique Przemsza River basin, which differs from other river basins in terms of the thermal regime of its rivers and its tendencies for change. These changes are presented through the long period of 1961-2015, and additionally in two sub-periods, 1961-1994 and 1995-2015, differing with respect to the degree of the organisation of wastewater management. This was possible to demonstrate thanks to the high density of hydrological stations within a small area, which is rare, especially over such a long period. This work, therefore, fills the research gap on changes in the thermal regime of rivers caused by water management. Its results differ from those presented so far in the literature and expand the knowledge of this subject. In the years under consideration (1961-2015), the air temperature increased by 0.03 °C·per year^-1 on average. It was found that the temperature of rivers (or their sections) decreased by as much as -0.8 °C·year^-1. The greatest drops in temperature occurred in the winter season and amounted to a maximum of -0.12 °C·season^-1. In the first part of the analysed period (1961-1994), unnaturally high river temperatures caused by water management factors were recorded. In turn, in the years 1995-2015 there was a decrease in temperature caused by various technical operations carried out in the catchments, including the construction of modern sewage treatment plants, as well as limitation of the introduction of water into rivers from closed hard coal mines. This phenomenon is referred to as thermal renaturation. Maintaining positive changes in the thermal regime requires, inter alia, a further reduction in the amount of pollutants discharged into rivers by water discharges from mine drainage.

Citizen science: An alternative way for water monitoring in Hong Kong

Nowadays, citizen science has become increasingly popular, especially in Western countries. In Hong Kong, citizen science projects are mostly used for public education, while utilizing citizen scientists in published scientific research is very rare. On the other hand, with the increasing threats to global water security, Hong Kong requires new adaptation and strategy in facing the impairment of local freshwater systems. However, unfortunately, the number of full-scale urban river research appears to be declining. In this regard, citizen science can offer an alternative option as one of the new integrated water management strategies in Hong Kong. In this study, the water quality of seven rivers and streams in Hong Kong was studied monthly for two years by a group of citizen scientists. The main goal is to examine the reliability of data collected by citizen scientists by comparing it with the official data from the Environmental Protection Department of Hong Kong (EPD). Results show that the water temperature and conductivity data acquired by the citizen scientists were highly comparable to the official data. Also, moderate to strong correlations in water pH, turbidity, and dissolved oxygen levels were found between citizen scientists and official data. Since the citizen science data remained as high as 70% of relevance to the official data, we believe that this may serve as a supplement to the lacking official or professional water quality monitoring data in Hong Kong. Even though the use of volunteer data in water quality monitoring unavoidably exists with errors and bias, this study demonstrates a successful outcome of utilizing citizen science programme in urban river monitoring in Hong Kong.

Rethinking discretization to advance limnology amid the ongoing information explosion

Limnologists often adhere to a discretized view of waterbodies-they classify them, divide them into zones, promote discrete management targets, and use research tools, experimental designs, and statistical analyses focused on discretization. By offering useful shortcuts, this approach to limnology has profoundly benefited the way we understand, manage, and communicate about waterbodies. But the research questions and the research tools in limnology are changing rapidly in the era of big data, with consequences for the relevance of our current discretization schemes. Here, I examine how and why we discretize and argue that selectively rethinking the extent to which we must discretize gives us an exceptional chance to advance limnology in new ways. To help us decide when to discretize, I offer a framework (discretization evaluation framework) that can be used to compare the usefulness of various discretization approaches to an alternative which relies less on discretization. This framework, together with a keen awareness of discretization's advantages and disadvantages, may help limnologists benefit from the ongoing information explosion.

Involving citizens in hydrodynamic research: A combined local knowledge - numerical experiment on Lake Garda, Italy

Local knowledge on surface currents and transport patterns in Lake Garda is acquired through interviews among wind-surfers, sailors, fishermen, ferry boat drivers, firefighters nautical rescue team, and officers from the environmental protection agency. Data are collected by means of individual interviews and focus groups, analyzed for internal consistency and summarized in qualitative maps. Three-dimensional numerical simulations are performed using a one-way coupled atmospheric-hydrodynamic model and the results are compared with the observations of the interviewees. Through this combined effort, currents that were not evident to the scientific community, but are well-known to sailors and surfers, can now be recognized and physically understood, like the 'Corif' that flows along the eastern shore in summertime between late morning and afternoon, when wind blows from the south. The transport patterns are also identified, like the predominant east-to-west surface transport experienced by fishermen under storm events and floods, that is confirmed for northerly wind, and the west-to-east transport for southerly wind. Moreover, the trajectory of a drifting capsized boat is reproduced by the model and the dynamics of the accident (location and timing) are reconstructed in collaboration with the firefighters nautical rescue team of Trento and based on information from local newspapers and witnesses. This exercise demonstrates that the joint effort of the scientific community and local experts can produce advances in the understanding of large-scale hydrodynamic processes in lakes.

The value of long-term citizen science data for monitoring koala populations

The active collection of wildlife sighting data by trained observers is expensive, restricted to small geographical areas and conducted infrequently. Reporting of wildlife sightings by members of the public provides an opportunity to collect wildlife data continuously over wider geographical areas, at lower cost. We used individual koala sightings reported by members of the public between 1997 and 2013 in South-East Queensland, Australia (n = 14,076 koala sightings) to describe spatial and temporal trends in koala presence, to estimate koala sighting density and to identify biases associated with sightings. Temporal trends in sightings mirrored the breeding season of koalas. Sightings were high in residential areas (63%), followed by agricultural (15%), and parkland (12%). The study area was divided into 57,780 one-square kilometer grid cells and grid cells with no sightings of koalas decreased over time (from 35% to 21%) indicative of a greater level of spatial overlap of koala home ranges and human activity areas over time. The density of reported koala sightings decreased as distance from primary and secondary roads increased, indicative of a higher search effort near roads. Our results show that koala sighting data can be used to refine koala distribution and population estimates derived from active surveying, on the condition that appropriate bias correction techniques are applied. Collecting koala absence and search effort information and conducting repeated searches for koalas in the same areas are useful approaches to improve the quality of sighting data in citizen science programs.

Assessing the performance of a suite of machine learning models for daily river water temperature prediction

In this study, different versions of feedforward neural network (FFNN), Gaussian process regression (GPR), and decision tree (DT) models were developed to estimate daily river water temperature using air temperature (T_a), flow discharge (Q), and the day of year (DOY) as predictors. The proposed models were assessed using observed data from eight river stations, and modelling results were compared with the air2stream model. Model performances were evaluated using four indicators in this study: the coefficient of correlation (R), the Willmott index of agreement (d), the root mean squared error (RMSE), and the mean absolute error (MAE). Results indicated that the three machine learning models had similar performance when only T_a was used as the predictor. When the day of year was included as model input, the performances of the three machine learning models dramatically improved. Including flow discharge instead of day of year, as an additional predictor, provided a lower gain in model accuracy, thereby showing the relatively minor role of flow discharge in river water temperature prediction. However, an increase in the relative importance of flow discharge was noticed for stations with high altitude catchments (Rhône, Dischmabach and Cedar) which are influenced by cold water releases from hydropower or snow melting, suggesting the dependence of the role of flow discharge on the hydrological characteristics of such rivers. The air2stream model outperformed the three machine learning models for most of the studied rivers except for the cases where including flow discharge as a predictor provided the highest benefits. The DT model outperformed the FFNN and GPR models in the calibration phase, however in the validation phase, its performance slightly decreased. In general, the FFNN model performed slightly better than GPR model. In summary, the overall modelling results showed that the three machine learning models performed well for river water temperature modelling.

Citizen science evidence from the past century shows that Scottish rivers are warming

Salmonid species are highly sensitive to river water temperature. Although long-term river temperature monitoring is essential for assessing drivers of change in ecological systems, these data are rarely available from statutory monitoring. We utilized a 105-year citizen science data set of river water temperature from the River Spey, North-East Scotland, gathered during the fishing season (April-October) between 1912 and 2016. As there were gaps in the records we applied generalised additive models to reconstruct long-term daily river temperature in the fishing season from air temperature, cumulative air temperature, day length and runoff. For that, continuous hydrometeorological data have been obtained from statutory monitoring and process-based models. Long-term warming trends of river temperature, namely an increase of 0.2 K per decade after 1961, have been mostly related to increasing air temperature of the same magnitude. Indirect impacts of rising air temperatures include less snow accumulation and snow melt as well as earlier snow melt. The snow free period starts around 2 days earlier per decade throughout the study period and 7 days earlier per decade after 1965. Consequently, the contribution of snow melt and its cooling properties to river temperature in spring are declining. Citizen science delivered a data set that filled a vital knowledge gap in the long-term historical assessment of river temperatures. Such information provides a robust basis for future assessments of global change and can help inform decision-makers about the potential importance of enhancing the resilience of rivers and aquatic ecology to climate change.

Development of a Distributed Modeling Framework to Estimate Thermal Comfort along 2020 Tokyo Olympic Marathon Course

Heat stress is an issue for marathon races in the summer, such as the one planned for the 2020 Tokyo Summer Olympic games. The Tokyo Metropolitan Government is planning to grow existing street trees’ canopies to enlarge their shade to reduce air temperature and solar radiation. To formulate a baseline to assess the effect of street trees and buildings on human thermal comfort, Distributed-COMfort FormulA (D-COMFA), a prototype of a distributed computer model using a geographic information system (GIS) was developed. D-COMFA calculates the energy budget of a human body on a 1 m cell basis, using readily available datasets such as weather measurements and polygon data for street structures. D-COMFA was applied to a street segment along the marathon course in Tokyo on an hourly-basis on 9 August 2016, the hottest day in Tokyo in 2016. Our case study showed that the energy budget was positively related to the sky view factor, air temperature, and solar radiation. The energy budget was reduced on average by 26–62% in the shade throughout the day.