Our research experience lies in the broad areas of remote sensing, and data assimilation for improving land surface hydrology. Multi-satellite assimilation and modeling platforms provide robust and complete land surface datasets (i.e., hydrology, energy, carbon fluxes). These platforms build a comprehensive description of the earth processes that is geared toward accurately representing the complexity of natural and anthropogenic interactions in land surface processes. Most importantly, these platforms are suitable as decision support tools for applications across different aspects of earth system science.
Current Funded Projects
Hydrological Predictability and Prediction Skill Associated with Agricultural Practices
Soil Moisture dynamics contribute to the accuracy of sub-seasonal to seasonal forecasts. While natural processes driving soil moisture dynamics (such as precipitation-induced soil wetting) are included in global atmospheric circulation models, anthropogenic processes (such as irrigated agriculture) are rarely modeled. The impact of irrigation on soil moisture variability and soil evaporation depends on the irrigation technique. Innovations in irrigation infrastructure have allowed humanity to utilize previously inaccessible water resources, irrigation techniques, and planting practices to enhance agricultural productivity, often converting rain-fed croplands to irrigated lands. While agricultural intensification is a promising approach to meet the increasing food needs of humanity, it is still unclear to what extent the expansion of irrigated areas, and the related agricultural practices will affect the local, regional, and global climates via teleconnections across temporal scales.
Our primary hypothesis is that an improved representation of land atmosphere dynamics can be accomplished if irrigation processes are explicitly coupled within current state-of-the-art Earth Modeling Systems. Specifically, we hypothesize: H1) The explicit representation of agricultural practices allows for improved forecasts skill of the NASA sub-seasonal and seasonal models. H2) Through land-atmosphere feedback, agricultural practices may alter the local precipitation regime at the sub-seasonal and seasonal timescale, thereby locally changing the irrigation water needs and availability with the potential to reshape the distribution and extent of areas suitable for irrigation. This feedback may involve teleconnections between irrigation in one region and hydroclimatic conditions in another region.
To assess predictability and influence of irrigation practices, we propose to use both observations and model analyses. We will introduce an irrigation scheme in the existing GEOS Subseasonal to Seasonal (GEOS-S2S) modeling frameworks. By introducing the anthropogenic influence on the boundary layer regulating land-atmosphere feedback, we aim on improving S2S forecasts skill. We propose to analyze variables that might be influenced by irrigation practices (e.g., land surface temperature, soil moisture, evapotranspiration, and precipitation) and are readily available from satellite derived products. We will also carry out regional teleconnections assessment of forecast predictability due to the inclusion of agricultural practices. This will be done using statistical decomposition analysis on the ensemble of produced hindcasts.
The Role of Sierra Nevada Mountains in Regulating Central Valley Groundwater Recharge
California snowpack stores cold-season precipitation to meet water demand down-hill during the warm-season. Climate change threatens to disturb this cold-warm-season balance by altering the fraction of precipitation falling as snow and the timing of snowmelt. Groundwater storage (GWS) is one the most valuable freshwater resources as California’s Central Valley relies on groundwater pumped from deep aquifers and surface water transported from the Sierra Nevada to produce a quarter of the United States’ food demand. The natural recharge to deep aquifers is thought to be regulated by the adjacent high Sierra Nevada mountains, but the underlying mechanisms remain elusive. Models currently used for water management do not include mountain recharge processes and, while gravity observations from GRACE and GRACE-FO (here simply referred to as “GRACE”) missions have been used to quantify groundwater storage change over large aquifer systems, their spatial and temporal resolutions limit our ability to capture finer scale groundwater physical recharge processes. Methodological advancements are needed to fully exploit GRACE data for groundwater recharge estimates.
The overall objective of this proposed work is to understand and quantify, the feedback between Sierra Nevada seasonal snow, the GWS in the Central Valley, and the impact of hydro-climatic extremes such as droughts and atmospheric rivers on GWS, while reducing key uncertainties in the current physical understanding of the natural system for guiding hazard monitoring and predictions, thus advancing climate change adaption strategies. We leverage recent advances in satellite observations and modeling to answer the following overarching science question: How much water from the Sierra Nevada’s recharges aquifers in the Central Valley via mountain recharge processes, and at what rate?
We are working to: 1) increase spatial and temporal resolutions of the GRACE observations by integrating them with vertical land motion data to estimate GWS storage change in both deep and shallow aquifers; 2) develop a process-based modeling framework of the Sierra Nevada-Central Valley interface in which land-surface to GWS recharge mechanisms are explicitly represented; 3-4) quantify GWS recharge mechanisms and associated uncertainties by assimilating, among others, the novel set of observations derived in 1) within the modeling setup of 2).
This project is in collaboration with dr. Susanna Werth, dr. Erica Siirila-Woodburn, dr. Grace Carlson, and dr. Manoochehr Shirzaei
Combining multi-satellite observations, modeling and Earth system data assimilation for understanding observed and projected sea level change
While the main causes for sea level change are linked to ice melt and ocean thermal expansion, the contribution of hydrology (via river streamflow) has the power to affect the regional inter annual variability of the sea level. In general, global land surface models includes hydrologic response to climate driven processes (i.e., hydrology changes driven by precipitation, temperature, solar radiation), but anthropogenic processes (i.e., groundwater extraction, irrigation, impoundment in reservoir) are not yet modeled or assimilated in the reanalyses. To improve estimates of sea level rise we need to 1) improve model estimates of land hydrology by including both human and natural driven processes; 2) characterize their magnitude and uncertainty; 3) quantify land hydrology contribution (and uncertainty) to changes in the sea level. This improved estimation can happen via assimilation of satellite based terrestrial water storage and soil moisture observations. Team webpage: https://sealevel.nasa.gov