Using stable isotopes to inform water resource management in forested and agricultural ecosystems.

Present and future climatic trends are expected to markedly alter water fluxes and stores in the hydrologic cycle. In addition, water demand continues to grow due to increased human use and a growing population. Sustainably managing water resources requires a thorough understanding of water storage and flow in natural, agricultural, and urban ecosystems. Measurements of stable isotopes of water (hydrogen and oxygen) in the water cycle (atmosphere, soils, plants, surface water, and groundwater) can provide information on the transport pathways, sourcing, dynamics, ages, and storage pools of water that is difficult to obtain with other techniques. However, the potential of these techniques for practical questions has not been fully exploited yet. Here, we outline the benefits and limitations of potential applications of stable isotope methods useful to water managers, farmers, and other stakeholders. We also describe several case studies demonstrating how stable isotopes of water can support water management decision-making. Finally, we propose a workflow that guides users through a sequence of decisions required to apply stable isotope methods to examples of water management issues. We call for ongoing dialogue and a stronger connection between water management stakeholders and water stable isotope practitioners to identify the most pressing issues and develop best-practice guidelines to apply these techniques.

Water level regime variation is a crucial driver for taxonomic, functional, and phylogenetic diversity in seasonally flooded tropical forests.

Floodplains contribute significantly to terrestrial ecosystem service provision but are also among the most vulnerable and degraded ecosystems worldwide. Heterogeneity in floodplain properties arises from variations in river-specific flood regimes, watershed characteristics, and valley morphology, influencing seasonally flooded forests' taxonomic, functional, and phylogenetic diversity. This study addresses persisting knowledge gaps in floodplain ecology, focusing on the seasonally dry tropics. We explore the relationships between flood regime, environmental conditions, vegetation composition, functional and phylogenetic diversity, and the impact of environmental variables on above-ground biomass (AGB) and ecological strategies. The study spans six rivers in southeastern Brazil's main river basins: Rio Grande and São Francisco. We identified five eco-units in each floodplain based on flooding regimes and surveyed six plots per eco-unit. We measured trees with DBH > 5 cm and collected functional traits, along with detailed soil, climate, and water level data. We calculated plot-level floristic composition, taxonomic, functional, and phylogenetic diversity, wood density, and AGB. Functional and phylogenetic dissimilarity were analyzed, and the effects of climate, soil, and hydrological variables were quantified using generalized linear mixed models. We show how flood frequency and duration affect floristic composition across the floodplains. Taxonomic and phylogenetic diversity responded to climate, soil, and hydrological variables, while functional diversity responded primarily to hydrological variables, emphasizing the role of environmental filtering. Hydrological seasonality, soil fertility, and flood regime emerged as key factors shaping community structure and ecological strategies in the studied seasonally flooded tropical forests. Plot-level AGB responded to phosphorus but not to climate or hydrological variables. The study also highlights functional and phylogenetic dissimilarities among eco-units and basins, indicating potential climate change impacts.

Citizen science approaches for water quality measurements.

Citizen science has become a widely used approach in water quality studies. Although there are literature reviews about citizen science and water quality assessments, an overview of the most commonly used methods and their strengths and weaknesses is still lacking. Therefore, we reviewed the scientific literature on citizen science for surface water quality assessments and examined the methods and strategies used by the 72 studies that fulfilled our search criteria. Special attention was given to the parameters monitored, the monitoring tools, and the spatial and temporal resolution of the data collected in these studies. In addition, we discuss the advantages and disadvantages of the different approaches used in water quality assessments and their potential to complement traditional hydrological monitoring and research.

The CrowdWater game: A playful way to improve the accuracy of crowdsourced water level class data.

Data quality control is important for any data collection program, especially in citizen science projects, where it is more likely that errors occur due to the human factor. Ideally, data quality control in citizen science projects is also crowdsourced so that it can handle large amounts of data. Here we present the CrowdWater game as a gamified method to check crowdsourced water level class data that are submitted by citizen scientists through the CrowdWater app. The app uses a virtual staff gauge approach, which means that a digital scale is added to the first picture taken at a site and this scale is used for water level class observations at different times. In the game, participants classify water levels based on the comparison of the new picture with the picture containing the virtual staff gauge. By March 2019, 153 people had played the CrowdWater game and 841 pictures were classified. The average water level for the game votes for the classified pictures was compared to the water level class submitted through the app to determine whether the game can improve the quality of the data submitted through the app. For about 70% of the classified pictures, the water level class was the same for the CrowdWater app and game. For a quarter of the classified pictures, there was disagreement between the value submitted through the app and the average game vote. Expert judgement suggests that for three quarters of these cases, the game based average value was correct. The initial results indicate that the CrowdWater game helps to identify erroneous water level class observations from the CrowdWater app and provides a useful approach for crowdsourced data quality control. This study thus demonstrates the potential of gamified approaches for data quality control in citizen science projects.