Estimating Evapotranspiration and Ecosystem Respiration

1 Estimating Evapotranspiration and Ecosystem Respiration 2 Jiancong Chen, Baptiste Dafflon, Anh Phuong Tran , Nicola Falco, and Susan S. Hubbard 3 Department of Civil and Environmental Engineering, University of California, Berkeley, CA, USA, Earth and 4 Environmental Sciences Area, Lawrence Berkeley National Laboratory, Berkeley, CA, USA, Depatment of Water 5 Resources Engineering and Technology, Water Resources Institute, 8, Phao Dai Lang, Dong Da, Hanoi, Vietnam 6 7 Abstract: Gradual changes in meteorological forcings (such as temperature and precipitation) are reshaping 8 vulnerable ecosystems, leading to uncertain effects on ecosystem dynamics, including water and carbon fluxes. 9 Estimating evapotranspiration (ET) and ecosystem respiration (RECO) is essential for analyzing the effect of climate 10 change on ecosystem behavior. To obtain a better understanding of these processes, we need to improve our estimation 11 of water and carbon fluxes over space and time, which is difficult within ecosystems where we have only sparse data. 12 In this study, we developed a hybrid predictive modeling approach (HPM) that integrates eddy covariance 13 measurements, physically-based model simulation results, meteorological forcings, and remote sensing datasets to 14 estimate evapotranspiration (ET) and ecosystem respiration (RECO) in high space-time resolution. HPM relies on a 15 deep learning algorithm-long short term memory (LSTM)-as well as direct measurements or outputs from physically16 based models. We tested and validated HPM estimation results at sites within various mountainous regions, given 17 their importance for water resources, their vulnerability to climate change, and the recognized difficulties in estimating 18 ET and RECO in mountainous regions. We benchmarked estimates of ET and RECO obtained from the HPM method 19 against measurements made at FLUXNET stations and outputs from the Community Land Model (CLM) at Rocky 20 Mountain SNOTEL stations. At the mountainous East River Watershed site in the Upper Colorado River Basin, we 21 explored how ET and RECO dynamics estimated from the new HPM approach vary with different vegetation and 22 meteorological forcings. The results of this study indicate that HPM is capable of identifying complicated interactions 23 among meteorological forcings, ET, and RECO variables, as well as providing reliable estimation of ET and RECO 24 across relevant spatiotemporal scales. With HPM estimation of ET and RECO at the East River Watershed, we found 25 that abiotic factors of temperature and radiation predominantly explained ET spatial variability; whereas RECO 26 variability was largely controlled by biotic factors, such as vegetation type. In general, our study demonstrated that 27 the HPM approach can circumvent the typical lack of spatiotemporally dense data needed to estimate ET and RECO 28 over space and time, as well as the parametric and structural uncertainty inherent in mechanistic models. While the 29 current limitations of the HPM approach are driven by the temporal and spatial resolution of available datasets (such 30 as NDVI), ongoing advances in remote sensing are expected to further improve accuracy and resolution of ET and 31 RECO estimation using HPM. 32