Crowdsourcing Methods for Data Collection in Geophysics: State of the Art, Issues, and Future Directions

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

Automated updating of land cover maps used in hydrological modelling

Urbanization and rapid growth in population are leading to development in flood plains. Natural absorbent lands are substituted by confined ones. As a result, hydrology changes in such catchments, and flood risk increases. In the regions of urban spread out, the changes in land cover need to be frequently updated, as input to hydrological models for better estimations of the discharges (i.e. run-offs). Data processing and model building are processes generally done manually through multiple software packages. These are often laborious and time consuming activities. An automated method was developed to incorporate updated land cover maps in a hydrological model. The procedure involves calculations of new model parameters (e.g. curve numbers) as per new land cover maps and further use them in the hydrological model in order to simulate catchment run-off resulted due to the change in land cover. Present paper presents the developed method of automating the ArcGIS geoprocessing by using the Python programing language and the ArcPy libraries of ArcGIS. The connection with the hydrological model was done using MATLAB, which has specialized API that can change the input files for the HEC-HMS model. Hence, HEC-HMS is updated according to newly calculated curve numbers as a result of geo-processing. A GUI was developed using the MATLAB, for introducing new land cover map into ArcGIS for geoprocessing, and for replacing the curve numbers in the hydrological model with new ones. Through the GUI, the HEC-HMS hydrological model is run automatically, and results of hydrographs are extracted

Crowdsourcing Methods for Data Collection in Geophysics: State of the Art, Issues, and Future Directions

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.Water Resource