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Abstract Vegetation plays a crucial role in atmosphere‐land water and energy exchanges, global carbon cycle and basin water conservation. Land Surface Models (LSMs) typically represent vegetation characteristics by monthly climatological indices. However, static vegetation parameterization does not fully capture time‐varying vegetation characteristics, such as responses to climatic fluctuations, long‐term trends, and interannual variability. It remains unclear how the interaction between vegetation and climate variability propagates into hydrologic fluxes and water resources. Multi‐source satellite data sets may introduce uncertainties and require extensive time for analysis. This study developes a deep learning surrogate for a widely used LSM (i.e., Noah) as a rapid diagnosic tool. The calibrated surrogate quantifies the impacts of time‐varying vegetation characteristics from multiple remotely sensed GVF products on the magnitude, seasonality, and biotic and abiotic components of hydrologic fluxes. Using the Upper Colorado River Basin (UCRB) as a test case, we found that time‐varying vegetation provides more buffering effect against climate fluctuation than the static vegetation configuration, leading to reduced variability in the abiotic evaporation components (e.g., soil evaporation). In addition, time‐varying vegetation from multi‐source remote sensing products consistently predicts smaller biotic evaporation components (e.g., transpiration), leading to increased water yield in the UCRB (about 14%) compared to the static vegetation scheme. We also highlight the interaction between dynamic vegetation parameterization and static parameterization (e.g., soil) during calibration. Parameter recalibration and a re‐evaluation of certain model assumptions may be required for assessing climate change impacts on vegetation and basin‐wide water resources. 

Photoelectrochemical (PEC) water splitting is a promising technology for green hydrogen production by harnessing solar energy. Traditionally, this sustainable approach is studied under light intensity of 100 mW/cm²mimicking the natural solar irradiation at the Earth’s surface. Sunlight can be easily concentrated using simple optical systems like Fresnel lens to enhance charge carrier generation and hydrogen production in PEC water splitting. Despite the great potentials, this strategy has not been extensively studied and faces challenges related to the stability of photoelectrodes. To prompt the investigations and applications, this work outlines the best practices and protocols for conducting PEC solar water splitting under concentrated sunlight illumination, incorporating our recent advancements and providing some experimental guidelines. The key factors such as light source calibration, photoelectrode preparation, PEC cell configuration, and long-term stability test are discussed to ensure reproducible and high performance. Additionally, the challenges of the expected photothermal effect and the heat energy utilization strategy are discussed. 

This paper describes challenges to automated bulk collection of temperature-controlled magnetic core loss data in the 1-20 MHz regime. Oil immersion is shown to alter the small signal impedance of ML91S and FR67 ferrite cores by more than 5% over part of their rated frequency ranges which prevents accurate estimation of core loss. Air-based thermal forcing is shown to be a viable alternative to oil for core temperature regulation in high frequency core loss testers. Temperature regulation to 25 ± 3°C is demonstrated on FR80 at 1 MHz up to 1.25 W of dissipation. 

Magnetic core loss measurement methods suitable for high-frequency sinusoidal excitations are currently time intensive or inherently suffer from flux drive limitations due to the costly and ill-suited radio frequency (RF) equipment utilized in the measurement. The recent development of automated testers has enabled the collection of large core loss data sets across a broad range of operating frequencies and flux densities. Improper loading of the RF power amplifiers utilized in these measurements makes the collection of accurate core loss data impractical above certain drive levels. In this paper, we develop and demonstrate an automatable core loss testing method which replaces the commonly employed RF power amplifier with a high frequency switching inverter. An analytical framework for assessing flux harmonics in the core is also presented. An experimental demonstration is performed in the range of 1-7MHz for the following materials: (1) Fair-Rite 67, (2) Fair-Rite 80, and (3) Proterial ML91S.