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Models play a pivotal role in advancing our understanding of Earth's physical nature and environmental systems, aiding in their efficient planning and management. The accuracy and reliability of these models heavily rely on data, which are generally partitioned into subsets for model development and evaluation. Surprisingly, how this partitioning is done is often not justified, even though it determines what model we end up with, how we assess its performance and what decisions we make based on the resulting model outputs. In this study, we shed light on the paramount importance of meticulously considering data partitioning in the model development and evaluation process, and its significant impact on model generalization. We identify flaws in existing data-splitting approaches and propose a forward-looking strategy to effectively confront the “elephant in the room”, leading to improved model generalization capabilities.

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.

Sensors are becoming ubiquitous in everyday life, generating data at an unprecedented rate and scale. However, models that assess impacts of human activities on environmental and human health, have typically been developed in contexts where data scarcity is the norm. Models are essential tools to understand processes, identify relationships, associations and causality, formalize stakeholder mental models, and to quantify the effects of prevention and interventions. They can help to explain data, as well as inform the deployment and location of sensors by identifying hotspots and areas of interest where data collection may achieve the best results. We identify a paradigm shift in how the integration of models and sensors can contribute to harnessing ‘Big Data’ and, more importantly, make the vital step from ‘Big Data’ to ‘Big Information’. In this paper, we illustrate current developments and identify key research needs using human and environmental health challenges as an example.

[1] The understanding of streamflow generation processes is vitally important in the management of water resources. In the absence of the data required to achieve this, Integrated Surface-Subsurface Hydrological Models (ISSHM) can be used to assist with the development of this understanding. However, the standard outputs from these models only enable elicitation of information about hydrological drivers and hydrological responses that occur at the same time. This generally limits the applicability of ISSHMs for the purposes of obtaining an improved understanding of streamflow generation processes to catchment areas that do not exhibit significant storage, travel times or flow depletion mechanisms. In order to overcome this limitation, a previously published Hydraulic Mixing-Cell (HMC) method is improved so that it can be used to follow surface water derived from direct rainfall and groundwater discharge to the stream and adjacent overland flow areas. The developed approach was applied to virtual experiments (based on the Lehstenbach catchment in southeastern Germany), which are composed of two ISSHMs of contrasting scales: (1) a riparian wetland of area 210 m2 and (2) a catchment of area 4.2 km2. For the two models, analysis of modeling results for a large storm event showed complex spatiotemporal variability in streamflow generation and surface water-groundwater interaction. Further analysis with the HMC method elucidated in-stream and overland flow generation mechanisms. This study showed within a modeling framework that identification and quantification of in-stream and overland flow generation better informed understanding of catchment functioning through decomposition of streamflow hydrographs, and analysis of spatiotemporal variability of flow generation mechanisms.