Year Established: 2014 Start Date: 2014-03-01 End Date: 2016-02-28
Total Federal Funds: $26,708 Total Non-Federal Funds: $58,646
Principal Investigators: Sayed Bateni
Abstract: Intense rainfall over the wet Montane Cloud Forests (MCFs) yields high watershed runoff, which supports water resources demands in downstream. However, the rate of land surface evapotranspiration, ET (the combined loss of water from the land surface to the atmosphere through the evaporation process and plant transpiration) over MCFs is remarkably high, which significantly reduces the amount of water that flows into surface streams and groundwater.Despite its importance, there are neither operational networks nor systematic monitoring stations with measurements that allow mapping ET over wet MCFs in Hawai’i.Indirect methods have been used to estimate ET from micrometeorological station and satellite data but they mostly depend on some limiting empirical assumptions. An alternative approach to mapping ET is proposed here that has several distinct advantages. It assimilates in situ and/or space-borne land surface temperature (LST) measurements into a dynamic model of surface energy balance. The approach does not assume knowledge of surface (soil and vegetation) control on ET. Nor does it assume surface influence on local turbulent transfer. Furthermore, the inversion of the surface energy balance using LST is generally ill-posed (the budget equation includes both LST and its tendency). As a result, most past studies make empirical closure assumptions like taking ground heat flux to be a constant fraction of net radiation. In this project, we circumvent such empirical approaches by posing a novel variational assimilation framework. The cost function consists of the LST misfit term and deviations of parameter estimates from prior values. The energy budget equation is adjoined to the cost function as a strong constraint. Efficient solution procedures (Euler-Lagrange) are available for such systems. The framework has been successfully tested for feasibility using field experiment data were independent ground-truth on ET is available. The method will be extended to assimilate rainfall data(in addition to LST) to improve ET estimates. Moreover, a posteriori uncertainty bounds on the land ET estimates will be provided. The project is a “Science Data Analysis and Modeling Research” proposal. The primary objective is to estimate daily ET over the native wet montane cloud forests in Hawai’i via assimilation of in situ and/or space-borne LSTand rainfall measurements.The estimation control variables are evaporative fraction (EF, non-dimensional fraction of available energy used in latent heat flux) and neutral heat transfer coefficient. These two factors are the primary uncertain controls on land ET that need to be estimated. We will use independent remotely sensed soil moisture data (soil moisture is not used in the assimilation) to test the patterns in EF retrievals. The ultimate goal is to close the water and energy budget to within reasonable accuracy using the retrieved land evaporation estimates. Effects of climate change on ET in Hawai’i are yet unknown, but changes could further impact water resources already being affected by reduced rainfall. The secondary goal of this project is to evaluate the impact of climate change on ET over wet MCFs in Hawai’i. Findings from this project will be the necessary first attempt to characterize the impact of global warming on ET and eventually sustainability of water resources in Hawai’i. Results of this study will be of vital importance for ournext proposal, which aims at characterizing the impact of climate change on ET over the entire State of Hawai’i.