Observation-Driven Mapping of the Linkages between the Terrestrial Water, Energy and Carbon Cycles
The exchange of water, energy, and carbon between the land, biosphere and atmosphere play a key role in the Earth’s climate. The terrestrial or land component of water, energy and carbon cycles are strongly linked and operate in a harmonized manner. For example, an increase in atmospheric carbon dioxide modifies the amount of vegetation biomass, thus altering ecosystem photosynthesis and transpiration (hence, heat exchange) rates. Land Surface Models used in hydrologic, ecological and climate models require accurate representation of the links between terrestrial cycles. The lack of direct observation of key variables that can quantify these linkages; result in uncertain projections from these models. Using satellite information on land surface state measurements of soil moisture, soil temperature and vegetation index, this project applies a novel observation-driven approach to diagnose and map the linkages at regional scale. Mapping the linkages across different seasons, ecological and environmental conditions advances understanding of how the terrestrial water, energy and carbon cycles are linked and operate in the real world. The observation-driven form of the linkages between the cycles can be used to guide improvements in the predictive capabilities of Land Surface/Earth System models and hence improve simulation of regional land surface fluxes, climate and climate projections.
This project is funded by NSF CAREER Program in Hydrological Sciences.
Coupled Estimation of Evapotranspiration and Recharge From Remotely Sensed Land Surface Moisture and Temperature
Evapotranspiration and recharge are fluxes at the land-atmosphere interface. These are two critical fluxes in the water cycle that play a pivotal role in global water, energy and biogeochemical cycles, crop productivity, sustainability of aquifers, ecosystem health and climate. Regional characterization of these fluxes is needed for the current and foreseeable range of applications in water resources (monitoring surface water bodies, aquifer recharge and discharge), in regional biogeochemical budgets, in operational numerical weather prediction (model initializations), in flood forecasting, and in drought hazards mitigation. However, there are no direct measurements – in situ or by remote sensing – that can allow any mapping or any global or regional estimation of these fluxes. The goal of this study is to quantify/map the patterns and dynamics of evapotranspiration and recharge flux using land surface state observations that are widely available across a range of spatial and temporal scales, landscapes and climates. In order to achieve this objective, we propose to develop and integrate state-of-the-art computational and data driven techniques to yield first order accurate estimates of key state and parameters (e.g., estimation control variables) of evapotranspiration and recharge flux from implicit information contained in the multi-platform remotely sensed Land Surface state observations of Temperature (LST) and Soil Moisture (SM).
This project is funded by NASA ROSES: New (Early Career) Investigator program in Earth Science.
Hydraulic parameter Estimation by Remotely-Sensed Surface Soil Moisture Observations With a Model Reduced Variational Data Assimilation Scheme
Soil moisture plays an important role in the global water cycle and impacts weather and climate, energy fluxes at the land surface, agricultural and irrigation management practices, food production and the organization of natural ecosystems and biodiversity. Accurate estimation of soil moisture pattern is of critical importance for land surface and land- atmosphere interaction modeling. Remotely sensed soil moisture only provides surface soil moisture, upper few centimeter of soil column, and not the soil moisture profile as required by the land surface models. In this work, the potential of using surface soil moisture measurements to retrieve soil moisture profile will be explored using a proposed reduced-order variational data assimilation, that is based on the Proper Orthogonal Decomposition (POD) model reduction technique. This research has produced international collaboration with colleagues at King Abdullah University (KAUST).
This project is funded by USGS, DC Water Resource Research Institute and NASA ROSES: New (Early Career) Investigator program in Earth Science.