Publication Date
5-2025
Date of Final Oral Examination (Defense)
11-22-2024
Type of Culminating Activity
Thesis
Degree Title
Master of Science in Hydrologic Sciences
Department
Geosciences
Supervisory Committee Chair
Alejandro N. Flores, Ph.D.
Supervisory Committee Member
Anna Bergstrom, Ph.D.
Supervisory Committee Member
Qifei Niu, Ph.D.
Supervisory Committee Member
David Huber, Ph.D.
Abstract
Evapotranspiration (ET) is a major, and largely unobserved, component of the terrestrial water budget. Transpiration, the component of ET that represents water loss from terrestrial vegetation, impacts soil moisture through plant water uptake, which in turn influences groundwater recharge, watershed storage, and ultimately streamflow. Transpiration is controlled by several different hydrometeorological variables including air temperature (AT), vapor pressure deficit (VPD), soil moisture (SM), precipitation (P), and available energy (AE), depending on the plant species, plant community, and larger watershed interactions. Simultaneous high-resolution observations of transpiration from representative vegetation types with hydrometeorological variables can facilitate better interpretation of remote sensing and model-based estimates of ET, and provide important context for local hydrologic processes. The Penman-Monteith equation provides a theoretical framework relating ET and these hydrometeorological controls.
This research aims to improve the understanding of the direction and magnitude of influence of each hydrometeorological variable in controlling transpiration in a semiarid, snow-dominated mountain watershed on seasonal time scales. We installed sap flow sensors in two mature Ponderosa pine trees in Dry Creek Experimental Watershed that were operated continuously for 14 months. Over this period, we obtained hourly observations of sap flow, AT, relative humidity (RH), SM, P, and longwave and shortwave solar radiation for calculating AE from a nearby weather station. Four machine learning (ML) algorithms were used to create predictive models of transpiration as a function of observed hydrometeorological variables. Predicted transpiration was evaluated using a root mean squared error (RMSE) value for the four ML approaches and these values varied between 0.001 and 0.92 L/hr. Predictive model performance was also assessed via other metrics such as mean absolute error (MAE), coefficient of determination (R2), and Nash-Sutcliffe Efficiency (NSE). These ML models were then used as input to a Shapley analysis (SHAP), an explainer method based on game theory that facilitates the assessment of the relative importance and direction of different predictors.
The SHAP analysis showed that AE, VPD, and AT were positively associated with transpiration, meaning that higher values of these variables were associated with higher transpiration. Meanwhile, surface SM and P were negatively associated with transpiration rates. Seasonal transpiration peaks occurred in late spring and summer as AE, VPD, and AT increased, and are at their lowest in the fall, winter, and early spring as P and SM increased. The ML models predicted -and SHAP visualized- that AE, VPD, and AT are the most important factors driving transpiration, and P and SM are the least important overall. Although somewhat counterintuitive, the relative unimportance of P and SM likely reflects: (1) our methodology only accounts for P as a predictor of sap flow in the same hour, which does not account for the potentially temporal lag between the arrival of precipitation and plant water use, (2) the use of SM data from a single sensor at a relatively shallow depth that may not reflect plant available water, and (3) the availability of only a single year for analysis. Despite limitations, this study illustrates that ML and SHAP approaches can be used conjunctively to understand complex relationships between transpiration and hydrometeorology. In this study, the combined use of ML and SHAP revealed that the relative importance of AE and VPD in driving transpiration is seasonally dependent. This approach could be applied more broadly in space and time, facilitating an improved understanding of hydrometeorological controls on transpiration at landscape or watershed scales. This improved understanding could give forest and resource managers improved insight into watershed and forest health.
DOI
https://doi.org/10.18122/td.2321.boisestate
Recommended Citation
Bishop, Delaney, "Hydrometeorological Controls on Transpiration in a Semiarid, Snow-Dominated Watershed" (2025). Boise State University Theses and Dissertations. 2321.
https://doi.org/10.18122/td.2321.boisestate