About this Research Topic

Background

Ecosystem functions and services related to the hydrological cycle have been under increasing pressure due to human interventions in the Earth's biological, chemical, and physical systems. Global warming, pollution, and changes in land cover and land use (e.g., urban sprawl, deforestation, etc.) cause dramatic changes in the hydrological processes, particularly in agroforestry ecosystems. Agricultural intensification and industrialization boost surface water and groundwater pollution and excessive consumption, thus triggering water scarcity in all sectors. There is growing concern about potential negative consequences on water quality and food security, which threaten social and economic stability in many parts of the world. Agroforestry systems help address growing food security and sustainability challenges.



The rapid global environmental changes require attention to address the full range of impacts of human activities on the hydrological processes, such as increasing water stress on forests and agricultural crops and flooding associated with heavy rainfall events, to name a few. Stakeholders and decision-makers call for practical and effective actions to quantify the impact of human interventions on eco-hydrological functions and to address ecosystem restoration. Comprehensive hydrological modeling in both data-rich and data-scarce environments, scenario-driven simulations, and data-driven approaches such as those based on machine and deep learning are of paramount importance to understand the land-atmosphere feedback and ecosystem dynamics (e.g., for supporting risk assessment of future water usage and sustainable land management through informed and evidence-based decisions). However, model performance strongly depends on robust and explicit data. Data assimilation, novel experiments, and innovative integration of cutting-edge ground- (e.g., in-situ sensor networks), proximal- (e.g., Cosmic Ray Neutron Probes), and remote sensing-based (UAV, airborne, spaceborne) Earth observation (EO) build the basis for enhancing the understanding of hydrological processes in response to a changing environment.



This Research Topic invites articles that present state-of-the-art and novel research investigations into anthropogenic impacts on the (eco-)hydrological cycle based on EO across different spatial scales (i.e., from plot to regional scales) focusing on agroforestry ecosystems. We welcome multidisciplinary studies that involve, among others, hydrology, meteorology, remote sensing, ecology, agriculture, and geology to:

• Conduct innovative experimental designs and establish new-generation monitoring infrastructures (e.g., multi-sensor, multi-scale approaches) to evaluate the near-real-time and short-term effects of anthropogenic disturbances on the dominant hydrological processes in the groundwater-soil-plant-atmosphere system;

• Apply advanced and simplified data-driven hydrological models to simulate the historical and projected long-term effects of anthropogenic disturbances on water budget across different spatial scales;

• Develop and apply scenario-based projections (best-case, business-as-usual, worst-case) for a quantitative evaluation of the impact of anthropogenic disturbances (climate and land-use change) on the ecosystem services related to the hydrological cycle;

• Identify functional and dynamic indicators to conduct (cross-)sectoral vulnerability and risk assessments;

• Apply upscaling and downscaling methods to describe the impact of anthropogenic disturbances on the ecosystem functioning across different spatial scales; and,

• Support sustainable land management by proposing cost-effective adaptation strategies to reach efficient ecological criteria (e.g., carbon sequestration, water demand-side and supply-side options, reduction of water contamination, reduction of fire and hydraulic risks).

Keywords: (eco-)hydrological modeling and monitoring, downscaling/upscaling, land-use/land-cover change, forestry, agriculture, (cross-)sectoral vulnerability and risk assessments, machine-learning algorithm, data assimilation, upscaling/downscaling approaches

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.

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