Abstract. Climate change is reshaping vulnerable ecosystems, leading to
uncertain effects on ecosystem dynamics, including evapotranspiration (ET)
and ecosystem respiration (Reco). However, accurate estimation of ET
and Reco still remains challenging at sparsely monitored watersheds,
where data and field instrumentation are limited. In this study, we
developed a hybrid predictive modeling approach (HPM) that integrates eddy
covariance measurements, physically based model simulation results,
meteorological forcings, and remote-sensing datasets to estimate ET and
Reco in high space–time resolution. HPM relies on a deep learning
algorithm and long short-term memory (LSTM) and requires only air temperature,
precipitation, radiation, normalized difference vegetation index (NDVI), and
soil temperature (when available) as input variables. We tested and
validated HPM estimation results in different climate regions and developed
four use cases to demonstrate the applicability and variability of HPM at
various FLUXNET sites and Rocky Mountain SNOTEL sites in Western North
America. To test the limitations and performance of the HPM approach in mountainous
watersheds, an expanded use case focused on the East River Watershed,
Colorado, USA. The results indicate HPM is capable of identifying
complicated interactions among meteorological forcings, ET, and Reco
variables, as well as providing reliable estimation of ET and Reco
across relevant spatiotemporal scales, even in challenging mountainous
systems. The study documents that HPM increases our capability to estimate
ET and Reco and enhances process understanding at sparsely monitored
watersheds.
Deep Learning Insights into Lanthanides Complexation Chemistry
