Climate change presents one of the most pressing global challenges, with far-reaching implications for both natural and human systems. Among the myriad consequences of climate change, the rise in climate extreme events has been well-documented through observational and modelling studies. These extremes, ranging from heavy precipitation and floods to heatwaves, wildfires, and droughts, exert profound and often devastating impacts on agriculture and the society. In this context, understanding the intricate connectivity between agricultural and hydrological systems under the influence of climate change has emerged as a critical research imperative, and necessitates a multidisciplinary approach.
Recent advancements in remote sensing, machine learning, and the development of process-based models offer immense potential for in-depth investigations into agriculture and hydrological interactions in a changing climate. This session aims to solicit and showcase research contributions that employ diverse methodologies, particularly Earth Observations, machine learning approaches, and numerical/statistical models, to monitor, simulate, and predict the interaction and feedback between agricultural and hydrological systems, spanning various spatiotemporal scales.
We invite researchers to engage in this session and contribute to the collective understanding of how climate change impacts the intricate relationship between agricultural and hydrological systems. Topics of interest include, but are not limited to:
1. Observational insights: Present observational evidence and case studies that shed light on observed climate extremes, their effects on agricultural systems, and hydrological responses.
2. Machine learning applications: Innovative applications of machine learning algorithms to analyze and predict the impacts of climate extremes on agriculture and hydrology.
3. Numerical and Statistical Modeling: Utilize numerical and statistical models to simulate and project future scenarios of climate-induced changes in agricultural and hydrological systems.
4. Scaling Effects: Investigate the interconnectedness of agricultural and hydrological systems across various spatial and temporal scales, elucidating regional and global implications.
5. Adaptation and resilience: Discuss strategies for adapting agriculture and hydrology to climate extremes, emphasizing the importance of building resilience in these systems.
Recent advancements in remote sensing, machine learning, and the development of process-based models offer immense potential for in-depth investigations into agriculture and hydrological interactions in a changing climate. This session aims to solicit and showcase research contributions that employ diverse methodologies, particularly Earth Observations, machine learning approaches, and numerical/statistical models, to monitor, simulate, and predict the interaction and feedback between agricultural and hydrological systems, spanning various spatiotemporal scales.
We invite researchers to engage in this session and contribute to the collective understanding of how climate change impacts the intricate relationship between agricultural and hydrological systems. Topics of interest include, but are not limited to:
1. Observational insights: Present observational evidence and case studies that shed light on observed climate extremes, their effects on agricultural systems, and hydrological responses.
2. Machine learning applications: Innovative applications of machine learning algorithms to analyze and predict the impacts of climate extremes on agriculture and hydrology.
3. Numerical and Statistical Modeling: Utilize numerical and statistical models to simulate and project future scenarios of climate-induced changes in agricultural and hydrological systems.
4. Scaling Effects: Investigate the interconnectedness of agricultural and hydrological systems across various spatial and temporal scales, elucidating regional and global implications.
5. Adaptation and resilience: Discuss strategies for adapting agriculture and hydrology to climate extremes, emphasizing the importance of building resilience in these systems.