From Satellites to Storage: Using Remote Sensing Data to Monitor Changes in Stored Groundwater
The Place
California’s Central Valley is one of the most productive agricultural regions in the world. It accounts for 9% of the value of crops grown in the United States on just 1% of its agricultural land area. However, record-setting droughts (e.g. 2012 - 2016) and future climate changes jeopardizes the food - water security of the region.
The Problem
Accurate estimates of changes in groundwater storage are critical to the sustainable management of worldwide aquifers, but few methods exist that can provide timely estimates of aquifer dynamics, especially in regions where groundwater monitoring wells are sparse. Groundwater shortages are increasingly impacting arid regions like California, South Asia and Africa as climate change alters the timing and duration of rainfall, snowmelt, and other hydrologic processes. Excessive pumping of groundwater, which is common during droughts, causes a number of negative externalities including: (1) dry domestic and agricultural wells, (2) damage to groundwater-dependent ecosystems, (3) permanent land subsidence, and (4) aquifer contamination by arsenic and other chemicals.
Our Approach
The proliferation of satellite remote sensing instruments and data allow us to measure hydrologic processes with unprecedented spatial resolution and temporal sampling frequency. By combining remotely-sensed data from a variety of instruments with on-the-ground measurements, we can rapidly estimate changes in regional groundwater storage. These estimates can be computed for any area, as long as sufficiently high-resolution data is available. Groundwater storage changes are calculated as the residual of inflows (precipitation, streamflow, runoff) minus outflows (evapotranspiration, streamflow), and corrected for fluctuations in reservoir levels, snowpack, and soil moisture. This mass balance approach facilitates efficient computation of changes in groundwater levels integrated over an area, and may be extensible to data-scarce regions of the world, since it relies heavily on freely available satellite data.
Results to Date
We developed a novel method that synthesizes satellite-based measurements of hydrologic properties to calculate changes in groundwater storage, as well as estimates of uncertainty, and demonstrated this method in three regions within California spanning 3 orders of magnitude in spatial scale by comparing our results to independent estimate derived from gravity data, groundwater wells, and groundwater models.
The resulting estimates of groundwater storage changes are able to capture trends, seasonality, and extreme events at each of the spatial scales, and exhibit strong agreement in terms of both trends and magnitudes with each of the independent validation datasets (Figure 2). Uncertainty quantified among water balance components demonstrated that evapotranspiration is the most uncertain component among inputs. This is unsurprising, given that the study areas are located in a highly irrigated region, which can complicate retrievals of evapotranspiration from certain remotely sensed models.
Project Sponsors
The Gordon and Betty Moore Foundation
Project Publications and Presentations
Ahamed, A., Knight, R., Alam, S., Pauloo, R. and Melton, F., 2022. Assessing the utility of remote sensing data to accurately estimate changes in groundwater storage. Science of The Total Environment, 807, p.150635. DOI: 10.1016/j.scitotenv.2021.150635
Ahamed, A., Knight, R., Pauloo, R., Melton, F., and Wei, Z. (2019). Remote Sensing-Based Estimation of Groundwater Storage Changes in California’s Central Valley. AGU Chapman Conference on Heavily Stressed Aquifers; Oct. 21-24, 2019, Valencia, Spain.