How Sierra Nevada snowpack confuses Central Valley groundwater readings

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2D strain map derived from synthetic aperture interferometric radar data over the Central Valley of California, USA, for the period January 1, 2015 through September 19, 2019. Credit: Geophysics research letters (2023). DOI: 10.1029/2023GL103222

Billions of tons of snow accumulated atop the Sierra Nevada Mountains may cause parts of the Central Valley, just west of the range, to sink, confusing groundwater assessments that see sinking as a sign of depleted aquifers. A recent study from Stanford University now offers a way to explain this heavy mountain snowpack and more accurately measure groundwater levels.

Analysis of satellite measurements of surface changes over time has emerged as a promising method for monitoring groundwater in places like the agriculturally rich Central Valley, where farmers rely heavily on groundwater to irrigate crops in dry years . But the method requires a clear understanding of the true mechanisms behind any observed elevation changes.

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The new study, published April 28 in Geophysics research letters, shows how snow and ice accumulated in the Sierra during California’s rainy season depresses the valley floor, accounting for most of the elevation change seen in the 60% of the valley. When tens or even hundreds of feet of snow accumulates in the Sierra, as it did in historic fashion last winter, the ground in the valley below sinks a tenth of an inch to an inch.

Although scientists have long suspected that snow and ice in nearby mountains could impair groundwater assessments linked to elevation changes, they didn’t have a way to quantify the effect. “We have shown for the first time how to untangle, decouple and ultimately isolate the two effects of elevation changes due to groundwater levels and snowpack load,” said lead author of the study Seogi Kang, postdoctoral fellow in geophysics at Stanford.

Failure to properly account for the effect of snowpack loading could lead groundwater managers, whose decisions are increasingly informed by altitude-based monitoring methods, to underestimate actual water levels.

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“With the weather extremes of floods and droughts becoming more common due to climate change, coupled with the challenge of ensuring the long-term sustainability of our groundwater resources, providing groundwater managers with the latest technology and knowledge is critical said study senior author Rosemary Knight, a professor of geophysics at the Stanford Doerr School of Sustainability. “This study is a major step in providing groundwater managers with a new way to use satellite data to accurately monitor the volume of groundwater stored in the Central Valley.”

The top view

For the study, Kang and Knight looked at more than five years of elevation data collected using a technique known as interferometric synthetic aperture radar, or InSAR, which works by measuring the time it takes for radar signals to bounce off a satellite from a series of locations. precise. on the ground at different times.

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Analyzing this data over time can reveal insights into groundwater resources because the underground layers of sediment and clay act like a sponge. If pumping groundwater for irrigation and drinking water depletes an aquifer, it’s like squeezing the sponge: the strata compact and can cause the ground surface to sink.

The InSAR dataset used for the new study covers nearly all of the Central Valley’s 18,000 square miles, with measurements taken on average once a week between 2015 and 2019. It is far better than common groundwater monitoring methods that involve drilling sporadic wells across the vast Central Valley and checking the readings a few times a year.

The snowpack effect

Kang built a statistical machine learning program to sift through the immense array of InSAR data into similar clusters based on the temporal variation of the data. These data were then compared with monthly average snowpack data for the Sierra Nevada for the same time period. “Sorting the data this way allowed us to identify which parts of the Central Valley where elevation changes are dominated by hydrological loading, what we call the snowpack effect, and which parts are dominated by the groundwater system.” Kang said.

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Overall, by filling major data gaps in the current groundwater monitoring regimen using InSAR data, the Stanford researchers hope their approach will support and inform groundwater management decisions, as to where and when to limit the pumping as best as possible to allocate new water delivery infrastructure.

“Much of the food we eat and enjoy comes from the Central Valley and other places that face significant water supply challenges,” Kang said. “With a better understanding of the hydrogeophysics at play here, we can help ensure that agricultural productivity in the Central Valley remains sustainable.”

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More information:
S. Kang et al, isolating the poroelastic response of the groundwater system in InSAR data from the Central Valley of California, Geophysics research letters (2023). DOI: 10.1029/2023GL103222

About the magazine:
Geophysics research letters

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