Assimilation of GRACE Terrestrial Water Storage Data into a Land Surface Model: Results for the Mississippi River Basin

Abstract

Popular Summary: NASA's GRACE mission has the potential to be extremely valuable for water resources applications and global water cycle research. What makes GRACE unique among Earth Science satellite systems is that it is able to monitor variations in water stored in all forms, from snow and surface water to soil moisture to groundwater in the deepest aquifers. However, the space and time resolutions of GRACE observations are coarse. GRACE typically resolves water storage changes over regions the size of Nebraska on a monthly basis, while city-scale, daily observations would be more useful for water management, agriculture, and weather prediction. High resolution numerical (computer) hydrology models have been developed, which predict the fates of water and energy after they strike the land surface as precipitation and sunlight. These are similar to weather and climate forecast models, which simulate atmospheric processes. We integrated the GRACE observations into a hydrology model using an advanced technique called data assimilation. The results were new estimates of groundwater, soil moisture, and snow variations, which combined the veracity of GRACE with the high resolution of the model. We tested the technique over the Mississippi River basin, but it will be even more valuable in parts of the world which lack reliable data on water availability. horizontally disaggregating GRACE data. At smaller spatial scales, the performance of the assimilation system was highly sensitive to the level of error ascribed to the GRACE observations. Spurious spikes appeared in the water storage estimates as the GRACE observation error estimate was reduced in the assimilation. The spikes should be addressed in future more sophisticated versions of the assimilation algorithm.

Groundwater Storage Changes: Present Status from GRACE Observations

“…Due primarily to the sparseness of soil moisture observations, quantifying groundwater change using GRACE time-variable gravity measurements often depends on accurate removal of SSS water storage changes by subtracting modeled estimates (Rodell et al 2007) or by assimilating GRACE TWS data into a land surface model (Zaitchik et al 2008). This is one of the major challenges limiting effective application of GRACE data to study GWS changes.…”

“…Decorrelation filter and 300 km Gaussian smoothing were applied to GRACE data, and 300 km Gaussian smoothing was applied to WGHM data Surv Geophys (2016) 37:397-417 401 An alternative to applying Eq. (3) with modeled SSS data is to incorporate GRACE TWS observations into a land surface model via data assimilation and allow the model to separate the TWS components (Zaitchik et al 2008). This approach is only appropriate if the model explicitly represents groundwater storage.…”

Assimilation of GRACE terrestrial water storage into a land surface model: Evaluation and potential value for drought monitoring in western and central Europe

“…Europe where climate and hydrological conditions differ significantly from the Mississippi area 120 studied by Zaitchik et al (2008). As droughts are common in Europe, the unique ability of 121 GRACE TWS to detect droughts and its potential for drought monitoring are considered in some 122 detail.…”

Modelling Freshwater Resources at the Global Scale: Challenges and Prospects

“…The most widely adopted technique is the ensemble Kalman filter (EnKF) or Smoother, where the otherwise unknown error characteristics of the model are estimated by a Monte Carlo-based ensemble approach to determine the error covariance matrix of the model. First assimilation studies using GRACE TWS show its value for informing simulated subsurface water storage (Zaitchik et al 2008;Li et al 2012;Houborg et al 2012). Eicker et al (2014) presented an EnKF approach for assimilating GRACE TWS into WaterGAP with combined state and parameter updating and a full error propagation from the monthly GRACE spherical harmonic coefficients.…”