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

Citizen science projects have a long history in ecological studies. The research usefulness of such projects is dependent on applying simple and standardized methods. Here, we conducted a citizen science project that involved more than 3500 Swedish high school students to examine the temperature difference between surface water and the overlying air (Tw-Ta) as a proxy for sensible heat flux (QH). If QH is directed upward, corresponding to positive Tw-Ta, it can enhance CO2 and CH4 emissions from inland waters, thereby contributing to increased greenhouse gas concentrations in the atmosphere. The students found mostly negative Tw-Ta across small ponds, lakes, streams/rivers and the sea shore (i.e. downward QH), with Tw-Ta becoming increasingly negative with increasing Ta. Further examination of Tw-Ta using high-frequency temperature data from inland waters across the globe confirmed that Tw-Ta is linearly related to Ta. Using the longest available high-frequency temperature time series from Lake Erken, Sweden, we found a rapid increase in the occasions of negative Tw-Ta with increasing annual mean Ta since 1989. From these results, we can expect that ongoing and projected global warming will result in increasingly negative Tw-Ta, thereby reducing CO2 and CH4 transfer velocities from inland waters into the atmosphere

Analysis of MERIS Reflectance Algorithms for Estimating Chlorophyll-a Concentration in a Brazilian Reservoir

Chlorophyll-a (chl-a) is a central water quality parameter that has been estimated through remote sensing bio-optical models. This work evaluated the performance of three well established reflectance based bio-optical algorithms to retrieve chl-a from in situ hyperspectral remote sensing reflectance datasets collected during three field campaigns in the Funil reservoir (Rio de Janeiro, Brazil). A Monte Carlo simulation was applied for all the algorithms to achieve the best calibration. The Normalized Difference Chlorophyll Index (NDCI) got the lowest error (17.85%). The in situ hyperspectral dataset was used to simulate the Ocean Land Color Instrument (OLCI) spectral bands by applying its spectral response function. Therefore, we evaluated its applicability to monitor water quality in tropical turbid inland waters using algorithms developed for MEdium Resolution Imaging Spectrometer (MERIS) data. The application of OLCI simulated spectral bands to the algorithms generated results similar to the in situ hyperspectral: an error of 17.64% was found for NDCI. Thus, OLCI data will be suitable for inland water quality monitoring using MERIS reflectance based bio-optical algorithms