Improving estimates of the surface terrestrial water and energy budgets

Abstract

Accurate estimates of the components of the surface terrestrial water and energy budgets are crucial to understand the interactions between land and atmosphere. These interactions play a critical role in modulating drought, heatwaves and other extreme events. In-situ measurements provide the most reliable direct estimates of these terms, however in many parts of the globe they are scarce or not available at all, and therefore cannot meet the spatial scale we need. The advancement of satellite technology and remote sensing retrieval algorithms has made it possible to estimate many fluxes from space, and led to the development of a suite of satellite-driven estimates of water and energy fluxes at the global gridded scale. These include entirely satellite-based estimates, reanalysis products, or model outputs driven by remotely-sensed surface properties. These global estimates have near complete spatial coverage, something lacking in in-situ observations. The aim of this thesis is to improve the current global gridded estimates of the water and energy fluxes by exploiting the accuracy of in-situ measurements and the coverage of existing, predominantly satellite-based gridded estimates, as well as physical relationships between them. A novel weighting technique is applied to derive hybrid estimates from a range of available satellite driven estimates of individual budget variables. It is an optimal merging method that accounts not only for performance differences between the participating products, but also the dependence of their errors, which has been ignored by the majority of similar attempts. This technique also provides rigorous estimates of uncertainty associated with the hybrid estimates that reflects their discrepancy with direct observations. We apply the merging technique to derive hybrid best estimates for net radiation, sensible, latent and ground heat flux and runoff. In a further post processing step, all the hybrid best estimates are further adjusted by enforcing the physical constraints of the water and energy balance. This process led to a novel suite of half degree gridded monthly hybrid estimates of water and energy budget terms. Component variables satisfy water and energy balance constraints simultaneously, provide uncertainty estimates consistent with ground-based observations, and compare more favorably with observations than any of the component products used to create them. In addition, the sensitivity of this approach to the employed datasets has given it a diagnostic utility which we use to evaluate multiple precipitation datasets and identify regions with less reliable estimates. An important finding is that physical constraints can complement in-situ observations to allow observation-based diagnostic evaluation of satellite-driven products in areas without direct observations.