Geology topics

Koong Yi

Publications and source records attributed to Koong Yi.

3 recordsLinked to original sources

USGS2026

Bioclimatic reorganization of carbon–water–energy coupling revealed by eddy covariance and interpretable machine learning

Ecosystem exchanges of carbon, water, and energy are central to Earth system functioning, yet their sensitivities to environmental variability remain poorly constrained across biomes and climates. Here, we analyzed ≥5 years of eddy covariance data from 87 AmeriFlux sites (964 site-years) spanning six vegetation types and a broad range of climatic conditions to examine the controls and multi-year trends of gross primary productivity (GPP), evapotranspiration (ET), water-use efficiency (WUE), and the Bowen ratio. We trained boosted regression tree ensembles with environmental (air temperature, vapor pressure deficit, soil water content, atmospheric CO₂, radiation, wind speed) and temporal (month, year) variables, and used interpretable machine learning—SHapley Additive exPlanations (SHAP) and Accumulated Local Effects (ALE)—to quantify driver importance and nonlinear responses. Across biomes and along climatic gradients, environmental controls reorganized predictably: radiation dominated under warm and humid conditions, whereas soil moisture exerted stronger influence in drier or warmer systems. GPP and ET were shaped by similar dominant drivers, with radiation and temperature generally strongest but soil moisture exerting comparable influence in some biomes. WUE was consistently constrained by vapor pressure deficit, reflecting stomatal regulation under rising atmospheric dryness. In contrast, controls on the Bowen ratio diverged more across biomes, reflecting heterogeneity in how ecosystems partition energy between latent and sensible heat. Multi-year trend analysis revealed negative associations of GPP and ET with rising atmospheric demand, and positive associations of the Bowen ratio with drying, indicating reduced evaporative cooling and stronger land–atmosphere coupling. Together, these findings show that while carbon and water fluxes remain tightly coupled across timescales, their balances reorganize predictably along bioclimatic gradients. This framework underscores the value of long-term flux networks and interpretable machine learning for benchmarking Earth system models and constraining projections of terrestrial carbon–water–energy dynamics under climate change.

Global Change Biology Communications↗

USGS2025

A novel approach to increase accuracy in remotely sensed evapotranspiration through basin water balance and flux tower constraints

Remote sensing-derived evapotranspiration (RSET) products capture the spatiotemporal variations of evapotranspiration (ET) from field to basin scales with unprecedented details. However, their accuracy varies across RSET estimation methods and diverse hydroclimate regions. While ET modeling efforts to account for biophysical processes and controlling parameters have made good progress in recent years, a parallel approach of integrating in-situ ET with RSET could reduce biases in RSET products. Basin water balance ET (WBET) and flux tower ET are widely applied to evaluate RSET accuracy, yet such ET measurements are rarely used for RSET bias corrections, especially for large area applications. To address this issue, we propose a novel approach: the water balance equivalence (WABE) method, which generates spatially continuous WBET for correcting biases in RSET products. The WABE method computes synthetic WBET by integrating observed WBET and flux tower-derived FLUXCOM ET, which fills the spatial gaps of observed WBET and generates a spatially continuous WBET dataset. Synthetic WBET (2002–2015 annual average) of eight-digit hydrologic unit code (HUC8) basins across the conterminous United States (CONUS), constituting 44 % (887 out of 2035 basins) of CONUS basins, was determined within 2.0 % (RMSE = 12 %) of observed WBET at CONUS and between 1–12 % (RMSE = 3–33 %) across 18 regions in CONUS. With WABE-based bias corrections, the overall annual bias of RSET decreased from 10 % (RMSE = 34 %) to 6 % (RMSE = 26 %) across 37 flux tower sites. The WABE method offers a new approach for RSET accuracy improvement and shows great promise for large area implementations with a potential to yield substantial benefits for building accurate basin water budgets and water management decisions.

Conterminous United States↗

USGS2024

Responses of marginal and intrinsic water-use efficiency to changing aridity using FLUXNET observations

According to classic stomatal optimization theory, plant stomata are regulated to maximize carbon assimilation for a given water loss. A key component of stomatal optimization models is marginal water-use efficiency (mWUE), the ratio of the change of transpiration to the change in carbon assimilation. Although the mWUE is often assumed to be constant, variability of mWUE under changing hydrologic conditions has been reported. However, there has yet to be a consensus on the patterns of mWUE variabilities and their relations with atmospheric aridity. We investigate the dynamics of mWUE in response to vapor pressure deficit (VPD) and aridity index using carbon and water fluxes from 115 eddy covariance towers available from the global database FLUXNET. We demonstrate a non-linear mWUE-VPD relationship at a sub-daily scale in general; mWUE varies substantially at both low and high VPD levels. However, mWUE remains relatively constant within the mid-range of VPD. Despite the highly non-linear relationship between mWUE and VPD, the relationship can be informed by the strong linear relationship between ecosystem-level inherent water-use efficiency (IWUE) and mWUE using the slope, m *. We further identify site-specific m * and its variability with changing site-level aridity across six vegetation types. We suggest accurately representing the relationship between IWUE and VPD using Michaelis–Menten or quadratic functions to ensure precise estimation of mWUE variability for individual sites.

Journal of Geophysical Research Biogeosciences↗