Benefits and Pitfalls of GRACE Terrestrial Water Storage Data Assimilation

Satellite observations of terrestrial water storage (TWS) from the Gravity Recovery and Climate Experiment (GRACE) mission have a coarse resolution in time (monthly) and space (roughly 150,000 sq km at midlatitudes) and vertically integrate all water storage components over land, including soil moisture and groundwater. Nonetheless, data assimilation can be used to horizontally downscale and vertically partition GRACE-TWS observations. This presentation illustrates some of the benefits and drawbacks of assimilating TWS observations from GRACE into a land surface model over the continental United States and India. The assimilation scheme yields improved skill metrics for groundwater compared to the no-assimilation simulations. A smaller impact is seen for surface and root-zone soil moisture. Further, GRACE observes TWS depletion associated with anthropogenic groundwater extraction. Results from the assimilation emphasize the importance of representing anthropogenic processes in land surface modeling and data assimilation systems

Improving Soil Moisture Estimation through the Joint Assimilation of SMOS and GRACE Satellite Observations

Observations from recent soil moisture dedicated missions (e.g. SMOS or SMAP) have been used in innovative data assimilation studies to provide global high spatial (i.e., approximately10-40 km) and temporal resolution (i.e., daily) soil moisture profile estimates from microwave brightness temperature observations. These missions are only sensitive to near-surface soil moisture 0-5 cm). In contrast, the Gravity Recovery and Climate Experiment (GRACE) mission provides accurate measurements of the entire vertically integrated terrestrial water storage (TWS) column but, it is characterized by low spatial (i.e., 150,000 km2) and temporal (i.e., monthly) resolutions. Data assimilation studies have shown that GRACE-TWS primarily affects (in absolute terms) deeper moisture storages (i.e., groundwater). In this presentation I will review benefits and drawbacks associated to the assimilation of both types of observations. In particular, I will illustrate the benefits and drawbacks of their joint assimilation for the purpose of improving the entire profile of soil moisture (i.e., surface and deeper water storages)

Rivers and Floodplains as Key Components of Global Terrestrial Water Storage Variability

This study quantifies the contribution of rivers and floodplains to terrestrial water storage (TWS) variability. We use stateoftheart models to simulate land surface processes and river dynamics and to separate TWS into its main components. Based on a proposed impact index, we show that surface water storage (SWS) contributes 8% of TWS variability globally, but that contribution differs widely among climate zones. Changes in SWS are a principal component of TWS variability in the tropics, where major rivers flow over arid regions and at high latitudes. SWS accounts for ~2227% of TWS variability in both the Amazon and Nile Basins. Changes in SWS are negligible in the Western U.S., Northern Africa, Middle East, and central Asia. Based on comparisons with Gravity Recovery and Climate Experimentbased TWS, we conclude that accounting for SWS improves simulated TWS in most of South America, Africa, and Southern Asia, confirming that SWS is a key component of TWS variability

Data Assimilation of Terrestrial Water Storage to Adjust Precipitation Fluxes

The Gravity Recovery and Climate Experiment (GRACE) mission has provided unprecedented observations of terrestrial water storage (TWS) dynamics at basin to continental scales. TWS is defined as the sum of groundwater, soil moisture, snow, surface water, ice and biomass water. Data assimilation of GRACE TWS observations has been shown to improve simulation of groundwater, streamflow, and snow water equivalent, and has also proven useful for drought monitoring and identifying human impacts on the water cycle. From a modeling perspective, the TWS components are defined as "prognostic hydrological states". Existing GRACE data assimilation schemes update these prognostic states directly. In this work, we propose an alternate approach in which precipitation fluxes are adjusted in order to achieve the desired change in the hydrological prognostic states. Limitations of such an approach include the assumption that all errors in TWS originate from errors in precipitation. Nonetheless, benefits comprise (1) the water balance is maintained, as opposed to having to add increments to the water budget components, (2) the model automatically determines how to distribute the updates among the TWS prognostic states, and (3) it is not necessary to know the exact time of the observation TWS, because the TWS change timing is determined by the precipitation forcing

Joint Assimilation of SMOS Brightness Temperature and GRACE Terrestrial Water Storage Observations for Improved Soil Moisture Estimation

Observations from recent soil moisture missions (e.g. SMOS) have been used in innovative data assimilation studies to provide global high spatial (i.e. 40 km) and temporal resolution (i.e. 3-days) soil moisture profile estimates from microwave brightness temperature observations. In contrast with microwave-based satellite missions that are only sensitive to near-surface soil moisture (0 - 5 cm), the Gravity Recovery and Climate Experiment (GRACE) mission provides accurate measurements of the entire vertically integrated terrestrial water storage column but, it is characterized by low spatial (i.e. 150,000 km2) and temporal (i.e. monthly) resolutions. Data assimilation studies have shown that GRACE-TWS primarily affects (in absolute terms) deeper moisture storages (i.e., groundwater). This work hypothesizes that unprecedented soil water profile accuracy can be obtained through the joint assimilation of GRACE terrestrial water storage and SMOS brightness temperature observations. A particular challenge of the joint assimilation is the use of the two different types of measurements that are relevant for hydrologic processes representing different temporal and spatial scales. The performance of the joint assimilation strongly depends on the chosen assimilation methods, measurement and model error spatial structures. The optimization of the assimilation technique constitutes a fundamental step toward a multi-variate multi-resolution integrative assimilation system aiming to improve our understanding of the global terrestrial water cycle

Ecosystem groundwater use enhances carbon assimilation and tree growth in a semi-arid Oak Savanna

Ecosystem reliance on groundwater, defined here as water stored in the saturated zone deeper than one meter beneath the surface, has been documented in many semi-arid, arid, and seasonally-dry regions around the world. In California, groundwater sustains ecosystems and mitigates mortality during drought. However, the effect of groundwater on carbon cycling still remains largely unresolved. Here we use 20 years of eddy covariance, groundwater, and tree growth measurements to isolate the impact of groundwater on carbon cycling in a semi-arid Mediterranean system in California during the summer dry season. We show that daily ecosystem groundwater use increases under positive groundwater anomalies and is associated with increased carbon assimilation and evapotranspiration rates. Negative groundwater anomalies result in significantly reduced ecosystem groundwater uptake, gross primary productivity, and evapotranspiration, with a simultaneous increase in water use efficiency. Three machine learning algorithms better predict gross primary productivity and tree growth anomalies when trained using groundwater data. These models suggest that groundwater has a unique effect on carbon assimilation and allocation to woody growth. After controlling for the effect of soil moisture, which is often decoupled from groundwater dynamics at the site, wet groundwater anomalies increase canopy carbon assimilation by 179.4 ± 25.7 g C m−2 (17 % of annual gross primary productivity) over the course of the summer season relative to dry groundwater anomalies. Similarly, annual tree growth increases by 0.175 ± 0.035 mm (17.7 % of annual growth) between dry and wet groundwater anomalies, independent of soil moisture dynamics. Our results demonstrate the importance of deep subsurface water resources to carbon assimilation and woody growth in dryland systems, as well as the benefits of collocated, long-term eddy covariance and ancillary datasets to improve understanding of complex ecosystem dynamics

DOPAL derived alpha-synuclein oligomers impair synaptic vesicles physiological function

Parkinson's disease is a neurodegenerative disorder characterized by the death of dopaminergic neurons and by accumulation of alpha-synuclein (aS) aggregates in the surviving neurons. The dopamine catabolite 3,4-dihydroxyphenylacetaldehyde (DOPAL) is a highly reactive and toxic molecule that leads to aS oligomerization by covalent modifications to lysine residues. Here we show that DOPAL-induced aS oligomer formation in neurons is associated with damage of synaptic vesicles, and with alterations in the synaptic vesicles pools. To investigate the molecular mechanism that leads to synaptic impairment, we first aimed to characterize the biochemical and biophysical properties of the aS-DOPAL oligomers; heterogeneous ensembles of macromolecules able to permeabilise cholesterol-containing lipid membranes. aS-DOPAL oligomers can induce dopamine leak in an in vitro model of synaptic vesicles and in cellular models. The dopamine released, after conversion to DOPAL in the cytoplasm, could trigger a noxious cycle that further fuels the formation of aS-DOPAL oligomers, inducing neurodegeneration

How Advection Affects the Surface Energy Balance and Its Closure at an Irrigated Alfalfa Field

Orbiting around the non-closure problem in eddy covariance, a new generation of high-resolution thermal imagery has revealed that advection may be more common than previously expected. To investigate this, we conducted an extensive study over an irrigated alfalfa field that experienced heat and moisture advection. Over the course of five analysis periods (37 days total), multiple tower arrays and profile measurements were deployed to measure the horizontal advection and vertical heat flux divergence. Latent heat flux (λE) measured at the anchor tower showed an enhancement (i.e., increase) due to both local and non-local processes. Locally, as a result of the upwind λE, advection humidified the atmosphere and increased stomatal opening, enhancing the downwind λE. Simultaneously, with lowered atmospheric demand, λE was suppressed downwind. Our results suggest that stomatal regulation played a dominant role in the enhancement, but not by itself. Spectral analysis revealed that low frequency (i.e., large) eddies contributed high heat and moisture via advection. In combination with thermal remote sensing observations from ECOSTRESS and Landsat 8/9, we found that these large eddies were generated over the upwind surface, and they were independent of the local boundary layer conditions. Consequently, spatiotemporal heterogeneity in land-surface conditions induced large eddies, further enhancing λE through non-local transport of heat and moisture. Lastly, by conditionally including the advective fluxes, the energy balance closure improved from 89 % to 97 % (r2 = 0.97, p \u3c 0.001) over the five analysis periods. Results from this improved energy balance closure suggest an alternative approach for developing validation datasets for remote sensing evapotranspiration (ET) models rather than forcing closure with Bowen-ratio. Furthermore, our findings provide insights for algorithms that may improve remote sensing ET products that treat pixels as isolated columns rather than also considering the lateral effects of heat and moisture transport

Estimation of Seasonal Snow Water Equivalent Using Landsat Observations

This work presents a methodology for estimating seasonal snow water equivalent (SWE) from the use of remotely sensed Visible and Near Infrared observations from the Landsat mission. The method is comprised of two main components: (1) a coupled land surface model and snow depletion curve model, which is used to generate an ensemble of predictions of SWE and snow cover area for a given set of (uncertain) inputs, and (2) a reanalysis step, which updates estimation variables to be consistent with the satellite observed depletion of the fractional snow cover time series. This method was applied over the Sierra Nevada (USA) based on the assimilation of remotely sensed fractional snow covered area data over the Landsat 5-8 record (1985-2016). The verified dataset (based on a comparison with over 9000 station years of in situ data) exhibited mean and root-mean-square errors less than 3 and 13 cm, respectively, and correlations with in situ SWE observations of greater than 0.95. The method (fully Bayesian), resolution (daily, 90-meter), temporal extent (32 years), and accuracy provide a unique dataset for investigating snow processes. In particular, this presentation illustrates how the reanalysis dataset was used to provide climatology of the seasonal snowfall accumulation rates, distributions, and variability over the last three decades