Data are essential in all areas of geophysics. They are used to better understand and manage systems, either directly or via models. Given the complexity and spatiotemporal variability of geophysical systems (e.g., precipitation), a lack of sufficient data is a perennial problem, which is exacerbated by various drivers, such as climate change and urbanization. In recent years, crowdsourcing has become increasingly prominent as a means of supplementing data obtained from more traditional sources, particularly due to its relatively low implementation cost and ability to increase the spatial and/or temporal resolution of data significantly. Given the proliferation of different crowdsourcing methods in geophysics and the promise they have shown, it is timely to assess the state of the art in this field, to identify potential issues and map out a way forward. In this paper, crowdsourcing‐based data acquisition methods that have been used in seven domains of geophysics, including weather, precipitation, air pollution, geography, ecology, surface water, and natural hazard management, are discussed based on a review of 162 papers. In addition, a novel framework for categorizing these methods is introduced and applied to the methods used in the seven domains of geophysics considered in this review. This paper also features a review of 93 papers dealing with issues that are common to data acquisition methods in different domains of geophysics, including the management of crowdsourcing projects, data quality, data processing, and data privacy. In each of these areas, the current status is discussed and challenges and future directions are outlined.

A bifunctional MoS2-based solar evaporator was designed for both efficient water evaporation and clean freshwater collection.

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Hydrological models are used for a wide variety of engineering purposes, including streamflow forecasting and flood‐risk estimation. To develop such models, it is common to allocate the available data to calibration and evaluation data subsets. Surprisingly, the issue of how this allocation can affect model evaluation performance has been largely ignored in the research literature. This paper discusses the evaluation performance bias that can arise from how available data are allocated to calibration and evaluation subsets. As a first step to assessing this issue in a statistically rigorous fashion, we present a comprehensive investigation of the influence of data allocation on the development of data‐driven artificial neural network (ANN) models of streamflow. Four well‐known formal data splitting methods are applied to 754 catchments from Australia and the U.S. to develop 902,483 ANN models. Results clearly show that the choice of the method used for data allocation has a significant impact on model performance, particularly for runoff data that are more highly skewed, highlighting the importance of considering the impact of data splitting when developing hydrological models. The statistical behavior of the data splitting methods investigated is discussed and guidance is offered on the selection of the most appropriate data splitting methods to achieve representative evaluation performance for streamflow data with different statistical properties. Although our results are obtained for data‐driven models, they highlight the fact that this issue is likely to have a significant impact on all types of hydrological models, especially conceptual rainfall‐runoff models.

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Hydrophobic magnetic microspheres can self-assemble into a thin film and float on the surface of water. The formed film was used as a photothermal material for water evaporation based on a new concept of interfacial solar heating.

Recently, solar evaporators composed of photothermal materials and their carriers have been designed and produced to enhance the solar evaporation rates based on interfacial solar heating. However, maintaining the high evaporation rate while preventing salt accumulation remains a challenge. In this paper, a water transport channel was designed to move the brine outside the solar evaporator to the expandable polyethylene (EPE) foam around the evaporator, thereby preventing salt accumulation in the evaporator. The concentration of the treated seawater was not increased during continuous evaporation and therefore avoiding the treatment of the high-concentration brine. The salt-rejecting solar evaporator was composed of a top layer of photothermal materials for high solar absorption, a thermal barrier layer of EPE foam for floatation and heat insulation, and a rationally designed water transport channel of air-laid paper (ALP) for fast seawater delivery to the top layer and outside the evaporator. The water evaporation rate of the simulated seawater by the salt-rejecting evaporator under 1 kW·m–2 solar irradiance was significantly enhanced to 1.46 kg·m–2·h–1 (accompanied by a photothermal conversion efficiency of 91.7%), which was 3.74 times higher than evaporation rate of the simulated seawater alone. The salt-rejecting evaporator also displayed excellent stability and durability as the evaporation rate was unchanged after 16 cycles of use. Finally, the potential application of the salt-rejecting evaporator was demonstrated in a practical setting by packing 25 evaporators in an EPE foam plate.

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