Ecology and Evolutionary Biology Commons^™

Evaluation Of Evapotranspiration Estimates Using An Existing Hybrid Machine Learning Model In A Natural And A Managed Dryland Site, Katya Esquivel Herrera

Open Access Theses & Dissertations

Evapotranspiration (ET) is a critical component of the hydrologic cycle, encompassing both evaporative water loss from surfaces and transpiration through plant stomata. The environmental factors influencing ET include water and energy availability, atmospheric capacity for water uptake, and various meteorological variables. ET serves as a unique climate variable linking water, energy, and carbon cycles. In agroecosystems, accurate ET quantification is vital for optimizing water use efficiency, irrigation management, and crop yield. Traditional methods for ET estimation involve direct measurements and indirect models, with both presenting limitations.

Recent years have witnessed the integration of remote sensing and machine learning (ML) algorithms …

Using Machine Learning And Distributed Hydrologic Modeling To Predict Soil Texture, Surface Soil Moisture And Evapotranspiration In Jornada Experimental Range, Southwestern U.S., Jorge Andres Mayo

Open Access Theses & Dissertations

In water-limited ecosystems, detailed knowledge of the soil, vegetation, and atmosphere interactions is critical to understand the processes that control the partitioning of energy, water fluxes, and biogeochemical cycles within the critical zone. This Master's thesis is divided into two main contributing sections. The first, is on the use of machine learning to reconstruct missing soil type information, and the second, on the calibration and validation of a physically-based distributed hydrological model to estimate soil moisture and evapotranspiration within the Jornada Experimental Range of the U.S. in southern New Mexico. For the first contribution, three explainable, shallow machine-learning techniques are …