About this Research Topic
Environmental processes, such as weather, climate, floods, droughts, and water pollution represent the spatiotemporal variations and changes in natural phenomena, with or without anthropogenic interventions. Most environmental processes are composed of several subprocesses, operating different spatial and temporal scales. Streamflow at the outlet of a watershed involves spatiotemporal meteorological and hydrological subprocesses, including rainfall, surface runoff, infiltration, channel network, and landform. Moreover, spatiotemporal variations of for instance storm rainfalls are affected by synoptic-scale climate, regional-scale weather, and local topographic patterns (the orographic effect). The complexity of environmental processes makes it impossible to observe all subprocesses with detailed spatial and temporal scales that allow for a complete description of the environmental process under investigation. Therefore, subprocess parameterization, spatial/temporal interpolation, and spatiotemporal downscaling have been exploited for modeling of environmental processes.
Understanding and quantifying the uncertainties of the models have also become an integrative part of environmental risk management. In environmental risk management, the major concern often lies in extremes and understanding the average processes is not sufficient. Thus, the chain of characterizing, modeling, forecasting, or simulating environmental processes is crucial for environmental risk assessment and management, and probabilistic modeling, i.e. modeling approaches that incorporate uncertainty into all stages of the modeling chain, are increasing in popularity.
In the era of big data, the amounts, sources, and varieties of data are increasing dramatically. It enables researchers to observe many complex environmental processes in multiple temporal and spatial scales. However, integration of environmental data from diverse sources, in different scales, and of different qualities, poses challenges. However, new methods of big data processing and extracting insightful knowledge from data are emerging and many governmental bodies and research institutes are already using big data to aid research and decision-making.
This Research Topic will provide a platform for researchers and engineers to share and discuss state-of-the-art scientific knowledge and best practices in spatiotemporal modeling and simulation of environmental processes. The focus is on modeling the spatial and/or temporal variation of environmental processes. We seek manuscripts that address any aspect of environmental process modeling and environmental risk management. This Research Topic includes, but is not limited to, the following themes:
• Understanding and modeling interactions between environmental subprocesses
• Theory and applications of spatiotemporal modeling and simulation
• Innovative methods for spatial and temporal downscaling
• Extreme weather and climate extremes, especially in relation to climate change
Keywords: Flood, Droughts, Climate Change, Downscaling, Spatiotemporal Modeling, Disaster Risk Management, Environmental Big Data
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
