Geology Reports⌕ Search
Home/Archive/USGS

USGS · 70032852

Joint variability of global runoff and global sea surface temperatures

G.J. McCabe·D.M. Wolock
View original source ↗Export metadata ↓

Abstract

Global land surface runoff and sea surface temperatures (SST) are analyzed to identify the primary modes of variability of these hydroclimatic data for the period 1905-2002. A monthly water-balance model first is used with global monthly temperature and precipitation data to compute time series of annual gridded runoff for the analysis period. The annual runoff time series data are combined with gridded annual sea surface temperature data, and the combined dataset is subjected to a principal components analysis (PCA) to identify the primary modes of variability. The first three components from the PCA explain 29% of the total variability in the combined runoff/SST dataset. The first component explains 15% of the total variance and primarily represents long-term trends in the data. The long-term trends in SSTs are evident as warming in all of the oceans. The associated long-term trends in runoff suggest increasing flows for parts of North America, South America, Eurasia, and Australia; decreasing runoff is most notable in western Africa. The second principal component explains 9% of the total variance and reflects variability of the El Ni??o-Southern Oscillation (ENSO) and its associated influence on global annual runoff patterns. The third component explains 5% of the total variance and indicates a response of global annual runoff to variability in North Aflantic SSTs. The association between runoff and North Atlantic SSTs may explain an apparent steplike change in runoff that occurred around 1970 for a number of continental regions.

Explore related subjects

Journal of HydrometeorologyArticle

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

G.J. McCabe, D.M. Wolock. 2008-08-01. Joint variability of global runoff and global sea surface temperatures. https://doi.org/10.1175/2008jhm943.1

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

USGS2026

Trends in subdaily to daily rainfall in Florida, 1990–2022

Changing rainfall patterns and intensifying rainfall extremes affect urban infrastructure and can increase flash-flood risk. Understanding how climate change has altered rainfall can support state and local agencies as they adapt and build resiliency. In this study, rainfall data from 23 weather stations in Florida were used to examine temporal and spatial trends over the period 1990–2022. Subdaily to daily rainfall events of durations 1, 2, 3, 6, 12, and 24 h were examined. A variety of statistical methods were applied to examine annual and seasonal trends, including quantile regression, extreme value analysis, run theory using the Mann–Kendall test, Sen–Theil slope, and Poisson and negative binomial tests, and threshold exceedance rates using generalized additive models. Using subdaily rainfall data posed challenges, including equipment failures, limited documentation of the quality assurance and control process, and potential measurement interferences. Results indicated that over 1990–2022, there was a decrease in hourly rainfall extremes but an increase at moderate quantiles. Overall, the number of rainfall events increased, particularly at shorter durations, but the mean total rainfall per event decreased. Additionally, the annual number of daily rainfall extremes showed more decreases than increases.

Florida↗
USGS2025

Alaskan hydrology in transition: Changing precipitation and evapotranspiration patterns are projected to reshape seasonal streamflow and water temperature by midcentury (2035-2064)

High spatial and temporal resolution models are essential for understanding future climate impacts and developing effective climate resilience plans. However, existing regional and global river models often lack the resolution needed to accurately capture local conditions. This study uses a series of high-resolution models, including the Regional Arctic System Model, mizuRoute, and the river basin model, to analyze Arctic and sub-Arctic Alaskan hydrology. We compare a historical baseline (1991–2020) with six midcentury (2035–64) futures: two pseudo–global warming scenarios based on historical meteorology and four direct dynamically downscaled global climate models. The six futures reveal significant uncertainty in future annual discharge and peak flows, although a widespread increase in discharge during April (+63%) and October (+31%) is consistently shown across models. Projected increases in rain and shifting weather patterns lead to a transition from snow to rain in spring and autumn, reducing the fraction of snowmelt contributing to river discharge. Rising evapotranspiration moderates discharge changes, particularly in autumn, by offsetting precipitation increases. Average summer river temperatures are projected to increase by approximately 1.5°C, doubling the number of river segments that experience 18°C days, a critical threshold for salmon survival, and intensifying the heat flux to the ocean adding an average of 3.3 × 10 12 MJ yr −1 . These changes in the hydrologic cycle could profoundly impact riverine and oceanic ecosystems, posing substantial challenges to communities reliant on these environments.

Alaska, Yukon↗
USGS2025

Wet antecedent soil moisture increases atmospheric river streamflow magnitudes non-linearly

Atmospheric rivers (ARs) drive most riverine floods on the United States (U.S.) West Coast. However, estimating flood risk based solely on AR intensity and duration is challenging because precipitation phase, antecedent conditions, and physical watershed characteristics (e.g., slope and soil depth) can influence the magnitude of floods. Here, we analyze how antecedent soil moisture (ASM) conditions contribute to variability in streamflow during AR events and how that changes across climatic regimes and physiography in 122 U.S. West Coast watersheds. We identify a robust non-linear relationship between streamflow and ASM during ARs in 89% of watersheds. The inflection point in this relationship represents a watershed-specific critical ASM threshold, above which event maximum streamflow is, on average, two to four and a half times larger. Wet ASM conditions amplify the hydrologic impacts of more frequent but weaker, lower moisture transport AR events, while dry ASM conditions attenuate the hydrologic impacts that stronger, higher moisture transport AR events could otherwise cause. Our research shows that watersheds prone to ASM-amplified streamflows have higher evaporation ratios, lower cold-season precipitation, lower snow-to-rain ratios, and shallower, clay-rich soils. Higher evaporation and lower precipitation lead to greater ASM variability during the cold season, increasing streamflow during wet periods and buffering streamflow during dry periods. Lower snow fraction and shallower soils limit the antecedent water storage capacity of a watershed, contributing to greater sensitivity of streamflow peaks to ASM variability. Incorporating ASM thresholds into hydrologic models in these regions prone to AR-amplified streamflow could improve forecasts and decrease uncertainty.

California, Nevada, Oregon, Washington↗