Ecohydrology
Understanding patterns and processes in dryland landscapes, from leaf-level processes to ecosystem-wide water cycles
Dryland Ecohydrology Research
Our ecohydrology research examines the complex interactions between water, vegetation, and climate in dryland ecosystems. We investigate how plants access, use, and respond to water across multiple scales, from individual leaves to entire landscapes, and how these processes are affected by environmental change.
Research Focus
Dryland ecosystems cover over 40% of Earth's land surface and support more than 2 billion people. These systems are characterized by water limitation and high climate variability, making them particularly vulnerable to environmental change.
Our research helps understand how these critical ecosystems function and respond to changing environmental conditions, informing conservation and management strategies.
Methodological Approach
We integrate field observations, remote sensing data, and mathematical modeling to understand ecohydrological processes across scales. Our work combines detailed physiological measurements with landscape-scale analysis.
Field sites span from the Kalahari Desert to East African savannas, providing insights into how dryland ecosystems function across different climatic and ecological contexts.
Key Research Areas
Our ecohydrology research spans multiple interconnected areas of investigation
Water Stress Dynamics
Plant physiological responses to water limitation, including stomatal regulation, osmotic adjustment, and hydraulic failure mechanisms in dryland species.
Vegetation Patterns
Spatial organization of vegetation in response to water availability, including self-organized patterns, patch dynamics, and landscape-scale heterogeneity.
Tree-Grass Dynamics
Competitive and facilitative interactions between woody and herbaceous vegetation, including savanna stability, encroachment processes, and coexistence mechanisms.
Climate Variability
Ecosystem responses to rainfall variability, drought events, and long-term climate change, including thresholds, resilience, and adaptation mechanisms.
Soil-Plant Interactions
Feedbacks between vegetation and soil properties, including nutrient cycling, soil moisture dynamics, and rhizosphere processes in water-limited environments.
Ecosystem Services
Quantification of ecosystem services provided by dryland systems, including carbon sequestration, biodiversity support, and hydrological regulation.
Recent Ecohydrology Publications
Latest research findings in dryland ecohydrology
Estimating whole-tree sap flow in buttressed and irregular trunks
Michael W. Burnett, Rayna Ruggeri, Kelly Caylor, Hillary S. Young, L. Anderegg (2026) • Agricultural and Forest Meteorology
Shifts in evapotranspiration components during heatwaves alter surface cooling
Han Chen, Stephen Good, E. Zahn, E. Bou-Zeid, Kelly Caylor, R.P. Fiorella, M. Haagsma, Lixin Wang (2026) • Earth's Future
Integrating physical processes with machine learning has advanced evapotranspiration (ET) simulation, yet most hybrid models fail to partition total ET into its components: soil evaporation (E) and vegetation transpiration (T). This study introduces Residual Neural Network–Penman–Monteith (RNN-PM), a novel hybrid dual-source ET model designed to overcome this limitation. The model synergizes the physically-based Penman–Monteith framework with three specialized residual neural networks trained to estimate key conductance parameters (canopy conductance, soil surface conductance, and aerodynamic conductance). This explicit parameterization allows for the direct partitioning of total ET. Validation at National Ecological Observatory Network (NEON) flux sites using high-frequency partitioned E and T shows that RNN-PM reliably reproduces ET and the transpiration fraction (T/ET). For ET, the model achieves an average Kling–Gupta efficiency (KGE) of 0.89 and a root-mean-square error (RMSE) of 0.55 mm/day; for T/ET, the KGE is 0.87 with an RMSE of 0.06. Furthermore, RNN-PM demonstrates robust generalization, accurately simulating ET and its components well beyond the initial training dataset, even under extreme climatic conditions. This study extended the analysis by comparing the RNN-PM model with seven established dual-source ET models. The results indicate that RNN-PM outperforms both conventional machine learning models and purely physical process-based models in simulating ET components in most cases. Among the purely physical process-based dual-source models, those based on surface temperature decomposition showed improved performance as the leaf area index (LAI) decreased when evaluated against high-frequency ET component datasets. In contrast, the performance of conductance-based dual-source models declined with decreasing LAI. Although purely machine learning-based models can produce relatively accurate simulations of ET components, they often exhibit limited generalization capability, an issue that the RNN-PM model effectively overcomes. Ultimately, the RNN-PM model represents a significant advance in simulating ET components, offering a novel and scalable approach for improving the representation of land–atmosphere interactions in Earth system models.
A hybrid Penman-Monteith and machine learning model for simulation evapotranspiration and its components
Han Chen, Stephen Good, Kelly Caylor, R. Fiorella, Lixin Wang (2026) • Journal of Hydrology
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Explore Our Research
Learn more about our other research themes and discover how ecohydrology connects with human systems and environmental sensing.