Exploring Hydro-Climatic Systems Through Data Analysis, Numerical Modeling, and Machine Learning
A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Meteorology".
Deadline for manuscript submissions: 31 March 2025 | Viewed by 91
Special Issue Editor
Interests: meteorology & atmospheric sciences; water resources environmental sciences & ecology; geology engineering
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Hydroclimatic data are generally collected through ground-based gauges and satellite observations. Recent advancements in remote sensing technologies—such as drones and satellite-based systems—have greatly improved our ability to monitor hydroclimatic variables like precipitation, temperature, and humidity. The high spatial and temporal resolution of these products is a notable achievement and has been applied across various disciplines. However, these estimates can still contain both systematic and random biases. Therefore, evaluating their accuracy and performance is crucial before using them for hydrological and water resource planning or decision-making.Integrating remotely sensed estimates with ground-based measurements and reanalysis data can significantly improve our understanding of hydroclimatic patterns, especially in data-scarce regions. Techniques such as downscaling and bias correction are also essential for acquiring localized information. Furthermore, advancements in artificial intelligence (AI) and machine learning (ML) are playing an increasingly important role in analyzing hydroclimatic data.
This Special Issue seeks to present cutting-edge research on using various hydroclimatic estimates to better understand hydroclimatic systems. Identifying the spatiotemporal distribution of hydroclimatic variables and their atmospheric mechanisms, particularly during extreme weather events, presents considerable challenges. Therefore, we encourage contributions that explore both the opportunities and challenges of using hydroclimatic data, with a focus on numerical models and machine learning techniques. Topics of interest include:
- Reviews of various satellite-based hydroclimatic products;
- Opportunities and challenges in utilizing these products in data-scarce regions;
- Bias correction and spatial downscaling of hydroclimatic estimates;
- Development of high-resolution space-time datasets for hydroclimatic variables;
- Application of numerical models (e.g., atmospheric, hydrological) using satellite-based data;
- Integration of machine learning and artificial intelligence in hydroclimatic analysis.
We look forward to your contributions to this vital area of research.
Dr. Rocky Talchabhadel
Guest Editor
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Keywords
- hydroclimate
- bias correction
- spatial downscaling
- numerical modeling
- extreme weather events
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