Advances in the Measurement, Utility and Evaluation of Precipitation Observations
This Reprint presents a collection of recent advances in the observation, measurement, and application of precipitation data in hydrological research. Precipitation is a central driver of the hydrological cycle, influencing processes such as runoff generation, groundwater recharge, flood forecasting...
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| Natura: | Online |
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| Lingua: | inglese |
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MDPI - Multidisciplinary Digital Publishing Institute
2026
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| Accesso online: | ONIX_20260416T142754_9783725857210_18 |
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| _version_ | 1869528007493812224 |
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| collection | Directory of Open Access Books |
| description | This Reprint presents a collection of recent advances in the observation, measurement, and application of precipitation data in hydrological research. Precipitation is a central driver of the hydrological cycle, influencing processes such as runoff generation, groundwater recharge, flood forecasting, and water resource management. Accurate and reliable precipitation information remains a cornerstone for understanding hydrologic variability and for developing effective mitigation and adaptation strategies in the face of climate change. The studies in this Reprint showcase a wide range of innovative approaches, from the integration of satellite and ground-based observations to the use of machine learning, interpolation, and regional calibration methods for improving rainfall estimates. Several contributions explore long-term precipitation trends, mathematical models to describe precipitation characteristics, storm dynamics in complex terrains, and the assessment of rainfall-related hazards. Others propose new techniques for downscaling, uncertainty quantification, and multi-source data fusion to enhance the spatial and temporal resolution of precipitation datasets. By highlighting methodological developments and practical applications across diverse climatic and geographic contexts, this Reprint provides valuable insights into how modern technologies and analytical frameworks are advancing the measurement and understanding of precipitation and its role in hydrologic systems. |
| format | Online |
| id | doab-20.500.12854ir-174913 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2026 |
| publishDateRange | 2026 |
| publishDateSort | 2026 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1749132026-04-16T17:19:47Z Advances in the Measurement, Utility and Evaluation of Precipitation Observations Zhang, Jiangjiang Jin, Junliang Precipitation observations Hydrological modeling Deep learning Rainfall-runoff Uncertainty quantification thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::P Mathematics and Science::PS Biology, life sciences This Reprint presents a collection of recent advances in the observation, measurement, and application of precipitation data in hydrological research. Precipitation is a central driver of the hydrological cycle, influencing processes such as runoff generation, groundwater recharge, flood forecasting, and water resource management. Accurate and reliable precipitation information remains a cornerstone for understanding hydrologic variability and for developing effective mitigation and adaptation strategies in the face of climate change. The studies in this Reprint showcase a wide range of innovative approaches, from the integration of satellite and ground-based observations to the use of machine learning, interpolation, and regional calibration methods for improving rainfall estimates. Several contributions explore long-term precipitation trends, mathematical models to describe precipitation characteristics, storm dynamics in complex terrains, and the assessment of rainfall-related hazards. Others propose new techniques for downscaling, uncertainty quantification, and multi-source data fusion to enhance the spatial and temporal resolution of precipitation datasets. By highlighting methodological developments and practical applications across diverse climatic and geographic contexts, this Reprint provides valuable insights into how modern technologies and analytical frameworks are advancing the measurement and understanding of precipitation and its role in hydrologic systems. 2026-04-16T17:19:40Z 2026-04-16T17:19:40Z 2025 book ONIX_20260416T142754_9783725857210_18 9783725857210 9783725857227 https://directory.doabooks.org/handle/20.500.12854/174913 eng application/octet-stream Attribution 4.0 International https://mdpi.com/books/ https://mdpi.com/books/pdfview/book/11795 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-5722-7 10.3390/books978-3-7258-5722-7 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725857210 9783725857227 230 CH open access |
| spellingShingle | Precipitation observations Hydrological modeling Deep learning Rainfall-runoff Uncertainty quantification thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::P Mathematics and Science::PS Biology, life sciences Advances in the Measurement, Utility and Evaluation of Precipitation Observations |
| title | Advances in the Measurement, Utility and Evaluation of Precipitation Observations |
| title_full | Advances in the Measurement, Utility and Evaluation of Precipitation Observations |
| title_fullStr | Advances in the Measurement, Utility and Evaluation of Precipitation Observations |
| title_full_unstemmed | Advances in the Measurement, Utility and Evaluation of Precipitation Observations |
| title_short | Advances in the Measurement, Utility and Evaluation of Precipitation Observations |
| title_sort | advances in the measurement utility and evaluation of precipitation observations |
| topic | Precipitation observations Hydrological modeling Deep learning Rainfall-runoff Uncertainty quantification thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::P Mathematics and Science::PS Biology, life sciences |
| topic_facet | Precipitation observations Hydrological modeling Deep learning Rainfall-runoff Uncertainty quantification thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::P Mathematics and Science::PS Biology, life sciences |
| url | ONIX_20260416T142754_9783725857210_18 |