Global quantitative estimations of ecosystem functions are vital. Among those, ecosystem respiration and photosynthesis contribute to carbon cycling and energy flow to food webs. These can be estimated in streams with the open channel diel oxygen method (single or two stations) essentially relying on a mass balance of oxygen over a defined reach. The method is generally perceived as low cost and easy to apply with new drift free optic sensors. Yet, it remains challenging on several key issues reviewed here: measurements of gas transfer at the air-water interface, appropriate mixing of tracers, uncertainty propagation in the calculations, spatial heterogeneity in oxygen concentrations, the derivation of net primary production (NPP) or autotrophic respiration, and the temperature dependence of photosynthesis and respiration. An extremely simple modeling tool is presented in an Excel workbook recommended for teaching the basic principles of the method. The only method able to deal with stream spatial heterogeneity is the method by Demars et al. Example data, Excel workbook, and R script are provided to run stream metabolism calculations. Direct gas exchange determination is essential in shallow turbulent streams, but modeling may be more accurate in large (deep) rivers. Lateral inflows should be avoided or well characterized. New methods have recently been developed to estimate NPP using multiple diel oxygen curves. The metabolic estimates should not be systematically temperature corrected to compare streams. Other recent advances have improved significantly the open channel diel oxygen method, notably the estimation of respiration during daylight hours.

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Google Trends (GT) offers a historical database of global internet searches with the potential to complement conventional records of environmental hazards, especially in regions where formal hydrometeorological data are scarce. We evaluate the extent to which GT can discern heavy rainfall and floods in Kenya and Uganda during the period 2014 to 2018. We triangulate counts of flood searches from GT with available rainfall records and media reports to build an inventory of extreme events. The Spearman rank correlation (ρ) between monthly mean search interest for flooding and monthly Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) rainfall totals was ρ = +0.38 (p < 0.005) for Kenya and ρ = +0.64 (p < 0.001) for Uganda. Media reports of flooding were used to specify a threshold of detectability to give the same overall frequency of floods based on GT search interest. When the GT search index threshold was set at ≥15 and ≥29, the correct detection rate was 75% and 64% within a five‐day window of known flood events in Kenya and Uganda, respectively. From these preliminary explorations we conclude that GT has potential as a proxy data source, but greater skill may emerge in places with larger search volumes and by linking to historical information about environmental hazards at sub‐national scales. Wider applicability of the GT platform might be possible if there is greater transparency about how Google algorithms determine topics.

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