• Energy Research
• 2025-2025close
• 6. Clean water close

Global lakes hold about 87% of the freshwater. However, climate change has posed a severe threat to these freshwater resources. Evaporation (E) is a major water loss from lakes, and the strong coupling between lake E and changes in atmospheric conditions in a warming climate leads to temporal and spatial variability in water loss through E, making it challenging for water resource management. This dissertation examines such spatiotemporal variability in global lake E in response to climate change, investigates its environmental controls, and identifies regions with large sensitivities to climate changes. Using a state-of-science Lake, Ice, Snow, and Sediment Simulator (LISSS) that is a lake model within the Community Land Model (CLM), it is shown that the large spatial variability of global lake E is modulated by the vapor pressure difference (e_D) between lake surface and overlying air. The e_D also causes higher nighttime lake E, which contributes more to the spatial variability of global lake E than daytime lake E. The performance of the Penman method (PM) is also evaluated against observations and the LISSS modeling results in estimating global lake E. It is shown that the PM overestimates lake E due to a strong bias in the net radiation (Rn) and lake water heat storage (G). Using the LISSS simulated Rn and G in the PM, however, the PM performance is largely improved and the PM E becomes comparable to the LISSS E. The global lake E trend over 1951 - 1978 is analyzed, which shows a decreasing E trend. Such a declined global lake E was largely caused by the decreased downward shortwave solar radiation. The global lake E was switched from the decreased trend over 1951-1978 to an increased trend over 1981-2016 with an accelerated trend of 0.76 mm yr-1. The tropical, arid, and temperate climate regions lakes contribute 66% to the increasing trend despite covering only 38% of the global lake surface area. Such a change in the global lake E trend was attributed to the increased vapor pressure deficit in a warmer climate. The model projection indicates that the mean global lake E will increase by 13% by the end of the 21st century under the Representative Concentration Pathway (RCP) 8.5 emissions scenario, relative to the 1985-2000 mean global lake E. The changes in lake E are expected to be more pronounced in North America, equatorial South America, Africa, northern Europe, Siberia, and Southeast Asia due to increased interannual variability. The results in this dissertation indicate that the widespread but heterogeneous increase in the global lake E threatens the crucial socioeconomic benefits that lakes provide to human society.

Machine learning's application in solar-thermal desalination is limited by data shortage and inconsistent analysis. This study develops an optimized dataset collection and analysis process for the representative solar still. By ultra-hydrophilic treatment on the condensation cover, the dataset collection process reduces the collection time by 83.3%. Over 1,000 datasets are collected, which is nearly one order of magnitude larger than up-to-date works. Then, a new interdisciplinary process flow is proposed. Some meaningful results are obtained that were not addressed by previous studies. It is found that Radom Forest might be a better choice for datasets larger than 1,000 due to both high accuracy and fast speed. Besides, the dataset range affects the quantified importance (weighted value) of factors significantly, with up to a 115% increment. Moreover, the results show that machine learning has a high accuracy on the extrapolation prediction of productivity, where the minimum mean relative prediction error is just around 4%. The results of this work not only show the necessity of the dataset characteristics' effect but also provide a standard process for studying solar-thermal desalination by machine learning, which would pave the way for interdisciplinary study.