Seasonality of Atmospheric River Frequency Depends on Location, Year, and Detection Algorithm DOI Creative Commons
Diya Kamnani, Travis O’Brien, Samuel Smith

и другие.

Authorea (Authorea), Год журнала: 2024, Номер unknown

Опубликована: Ноя. 23, 2024

Understanding the regional and temporal variability of atmospheric river (AR) seasonality is crucial for preparedness mitigation extreme events. Previously thought to peak mainly in winter, recent research reveals that ARs exhibit region-specific seasonality. However, AR analysis heavily influenced by chosen detection algorithm. Our study examines how varies based on both location, year algorithm selection. We investigate link between year-to-year consistency activity presence a dominant seasonal pattern. categorize regions their characteristics, including consistent patterns (e.g., India, Central Asia), with occasional outliers British Columbia coast, Gulf Alaska), lacking clear season frequency South Atlantic, parts Australia). Hence, not all cycle activity. Additionally, different algorithms may detect pattern same region but disagree exact season. This exemplified conflicting results obtained China. Integrated Vapor Transport (IVT) often corroborates or inconsistent across regions. In conclusion, this suggests variations are related only technique also circulation, synoptic low-frequency anomalies. areas like Britain remains challenging due algorithmic physical differences. These findings emphasize need multi-faceted approach research, considering just methodologies characteristics processes. specific reasons an important next step future improve forecasts preparedness.

Язык: Английский

Tracking California’s striking water storage gains attributed to intensive atmospheric rivers DOI
Zhongshan Jiang, Han Zhang, Miao Tang

и другие.

Journal of Hydrology, Год журнала: 2025, Номер unknown, С. 132804 - 132804

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

0
Significant increase and escalation of drought-flood abrupt alteration in China's future DOI Creative Commons
Shuai Zheng, Baisha Weng, Wuxia Bi

и другие.

Agricultural Water Management, Год журнала: 2025, Номер 312, С. 109449 - 109449

Опубликована: Март 30, 2025

Язык: Английский

Процитировано

0
Drought Characterization With GPS: Insights Into Groundwater and Surface‐Reservoir Storage in California DOI Creative Commons
Zachary M. Young, Hilary R. Martens, Zachary Hoylman

и другие.

Water Resources Research, Год журнала: 2024, Номер 60(8)

Опубликована: Авг. 1, 2024

Abstract Drought intensity is commonly characterized using meteorologically‐based metrics that do not provide insight into water deficits within deeper hydrologic systems. In contrast, global positioning system (GPS) displacements are sensitive to both local and regional hydrologic‐storage fluctuations. While a few studies have leveraged this sensitivity produce geodetic drought indices, characterization GPS accounted for in assessment management. To motivate application, we new index (GDI) quantify its ability characterize conditions key surface sub‐surface reservoirs/pools across California. northern California, the GDI exhibits strong association with surface‐reservoir storage at 1‐month time scale (correlation coefficient: 0.83) groundwater levels 3‐month 0.87), along moderate associations stream discharge daily (instantaneous) 0.50). Groundwater southern California best 12‐month 0.77), optimized 0.72). Two sigma uncertainties ±0.03. Differences between reveal unique aquifer drainage basin characteristics. addition capturing long‐term trends, rapid changes initiate during clusters of large atmospheric river events closely mirror fluctuations traditional meteorological observations. We show GPS‐based indices significant opportunity improve assessment, beyond, by improving our understanding cycle.

Язык: Английский

Процитировано

3
Seasonality of Atmospheric River Frequency Depends on Location, Year, and Detection Algorithm DOI Creative Commons
Diya Kamnani, Travis O’Brien, Samuel Smith

и другие.

Authorea (Authorea), Год журнала: 2024, Номер unknown

Опубликована: Ноя. 23, 2024

Understanding the regional and temporal variability of atmospheric river (AR) seasonality is crucial for preparedness mitigation extreme events. Previously thought to peak mainly in winter, recent research reveals that ARs exhibit region-specific seasonality. However, AR analysis heavily influenced by chosen detection algorithm. Our study examines how varies based on both location, year algorithm selection. We investigate link between year-to-year consistency activity presence a dominant seasonal pattern. categorize regions their characteristics, including consistent patterns (e.g., India, Central Asia), with occasional outliers British Columbia coast, Gulf Alaska), lacking clear season frequency South Atlantic, parts Australia). Hence, not all cycle activity. Additionally, different algorithms may detect pattern same region but disagree exact season. This exemplified conflicting results obtained China. Integrated Vapor Transport (IVT) often corroborates or inconsistent across regions. In conclusion, this suggests variations are related only technique also circulation, synoptic low-frequency anomalies. areas like Britain remains challenging due algorithmic physical differences. These findings emphasize need multi-faceted approach research, considering just methodologies characteristics processes. specific reasons an important next step future improve forecasts preparedness.

Язык: Английский

Процитировано

0