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.

This case study uses a long short-term memory (LSTM) based model to predict short-term urban water demands for the Hefei City of China. The performance of the LSTM based model is compared with autoregressive integrated moving average (ARIMA) model, the support vector regression (SVR) model and the random forests (RF) model based on data with time resolutions ranging from 15-minute to 24-hour. Additionally, this paper investigates the performance of the LSTM based model in predicting multiple successive data points. Results show that the LSTM based model can offer predictions with improved accuracy than the other models when dealing with data with high time resolutions, data points with abrupt changes and data of a relatively high uncertainty level. It is also observed that the LSTM based model exhibit the best performance in predicting multiple successive water demands with high time resolutions. In addition, the inclusion of external parameters (e.g., temperature) cannot enhance the performance of the LSTM based model, but it can improve ARIMAX's prediction ability (ARIMAX is the ARIMA with variables). These obtained insights based on the Hefei case study provide additional and improved knowledge as well as evaluations regarding the LSTM based models used for short-term urban water demand forecasting, thereby enabling their wider take-ups in practical applications.