Date of Award

Spring 1-1-2016

Document Type


Degree Name

Doctor of Philosophy (PhD)


Applied Mathematics

First Advisor

William Kleiber

Second Advisor

David C. Noone

Third Advisor

Keith Julien

Fourth Advisor

Jem Corcoran

Fifth Advisor

David Lawrence


The single largest uncertainty in climate model energy balance is the surface latent heating over tropical land. Furthermore, the partitioning of the total latent heat flux into contributions from surface evaporation and plant transpiration offers acute insight into the hydrological and biogeochemical behaviors of an ecosystem, but is notoriously difficult to establish directly. Evapotranspiration (ET) partitioning relies heavily on knowledge of the relative pathways by which water moves from the soil to the atmosphere. These pathways are parameterized by ecosystem resistances, which may not be known with great certainty in practical situations. Resolving these issues requires the development of statistical methods to maximize the use of limited information to best improve models. First, we introduce a commonly-used land surface model, the Community Land Model version 4 (CLM4). We describe an approach to calibrating select model parameters to observational data in a Bayesian estimation framework, requiring Markov chain Monte Carlo sampling of the posterior distribution. We demonstrate the ability of this Bayesian framework to constrain land-atmosphere exchanges of moisture and heat in CLM4, and yield an estimate of ET partitioning which is informed by data. Next, an isotopically-enabled version of CLM4 (iCLM4) is described in detail and validated using site-level and global observations. By leveraging the unique signatures of evaporation and transpiration on the ratios of stable water isotopes, additional constraint on the ET partitioning may be obtained. Finally, an extensive set of isotopic, meteorological and hydrological data from Erie, Colorado, USA is assimilated to calibrate land-atmosphere fluxes and state variables in iCLM4. It is demonstrated that the inclusion of water isotopic data in the assimilation step provides additional constraint on the estimated ET partitioning, and the benefits of these water isotopic datasets relative to common, non-isotopic datasets is quantified.