Small freshwater reservoirs are ubiquitous and likely play an important role in global greenhouse gas (GHG) budgets relative to their limited water surface area. However, constraining annual GHG fluxes in small freshwater reservoirs is challenging given their footprint area and spatially and temporally variable emissions. To quantify the GHG budget of a small (0.1 km2) reservoir, we deployed an eddy covariance system in a small reservoir located in southwestern Virginia, USA over two years to measure carbon dioxide (CO2) and methane (CH4) fluxes near-continuously. Fluxes were coupled with in situ sensors measuring multiple environmental parameters. Over both years, we found the reservoir to be a large source of CO2 (633-731 g CO2-C m-2 yr-1) and CH4 (1.02-1.29 g CH4-C m-2 yr-1) to the atmosphere, with substantial sub-daily, daily, weekly, and seasonal timescales of variability. For example, fluxes were substantially greater during the summer thermally-stratified season as compared to the winter. In addition, we observed significantly greater GHG fluxes during winter intermittent ice-on conditions as compared to continuous ice-on conditions, suggesting GHG emissions from lakes and reservoirs may increase with predicted decreases in winter ice-cover. Finally, we identified several key environmental variables that may be driving reservoir GHG fluxes at multiple timescales, including, surface water temperature and thermocline depth followed by fluorescent dissolved organic matter. Overall, our novel year-round eddy covariance data from a small reservoir indicate that these freshwater ecosystems likely contribute a substantial amount of CO2 and CH4 to global GHG budgets, relative to their surface area.

Spectral entropy (Hs) is an index which can be used to measure the structural complexity of time series data. When a time series is made up of one periodic function, the Hs value becomes smaller, while Hs becomes larger when a time series is composed of several periodic functions. We hypothesized that this characteristic of the Hs could be used to quantify the water stress history of vegetation. For the ideal condition for which sufficient water is supplied to an agricultural crop or natural vegetation, there should be a single distinct phenological cycle represented in a vegetation index time series (e.g., NDVI and EVI). However, time series data for a vegetation area that repeatedly experiences water stress may include several fluctuations that can be observed in addition to the predominant phenological cycle. This is because the process of experiencing water stress and recovering from it generates small fluctuations in phenological characteristics. Consequently, the value of Hs increases when vegetation experiences several water shortages. Therefore, the Hs could be used as an indicator for water stress history. To test this hypothesis, we analyzed Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data for a natural area in comparison to a nearby sugarcane area in seasonally-dry western Costa Rica. In this presentation, we will illustrate the use of spectral entropy to evaluate the vegetative responses of natural vegetation (dry tropical forest) and sugarcane under three different irrigation techniques (center pivot irrigation, drip irrigation and flood irrigation). Through this comparative analysis, the utility of Hs as an indicator will be tested. Furthermore, crop response to the different irrigation methods will be discussed in terms of Hs, NDVI and yield.

The magnitude and extent of runoff reduction, drought intensification, and dryland expansion under climate change are unclear and contentious. A primary reason is disagreement between global circulation models and current potential evaporation (PE) models for evaporative demand under warming climatic conditions. An emerging body of research suggests that current PE models including Penman-Monteith and Priestley-Taylor may overestimate future evaporative demand. However, they are still widely used for climatic impact analysis although the underlying physical mechanisms for PE projections remain unclear. Here, we show that current PE models diverge from observed non-water-stressed evaporation, a proxy of evaporative demand, across site (>1500 flux tower site years), watershed (>10,000 watershed-years), and global (25 climate models) scales. By not incorporating land-atmosphere feedback processes, current models overestimate non-water-stressed evaporation and its driving factors for warmer and drier conditions. To resolve this, we introduce a land-atmosphere coupled PE model that accurately reproduces non-water-stressed evaporation across spatiotemporal scales. We demonstrate that terrestrial evaporative demand will increase at a similar rate to ocean evaporation, but much slower than rates suggested by current PE models. This finding suggests that land-atmosphere feedbacks moderate continental drying trends. Budyko-based runoff projections incorporating our PE model are well aligned with those from coupled climate simulations, implying that land-atmosphere feedbacks are key to improving predictions of climatic impacts on water resources. Our approach provides a simple and robust way to incorporate coupled land-atmosphere processes into water management tools.

Although evapotranspiration (ET) from the land surface is a key variable in Earth systems models, the accurate estimation of ET based on physical principles remains challenging. Parameters used in current ET models are largely empirically based, which could be problematic under rapidly changing climatic conditions. Here, we propose a physically-based ET model that estimates ET based on the surface flux equilibrium (SFE) theory and the maximum entropy production (MEP) principle. We derived an expression for aerodynamic resistance based on the MEP principle, then propose a novel ET model that complementarily depends on the SFE theory and the MEP principle. The proposed model, which is referred to as the SFE-MEP model, becomes equivalent to the MEP state in non-equilibrium conditions when turbulent mixing is weak and the land surface is dry. On the contrary, the SFE-MEP model is similar to ET estimation based on the SFE theory in other conditions meeting land-atmosphere equilibrium. This feature of the SFE-MEP ET model allows accurate ET estimation for most inland regions by incorporating both equilibrium and non-equilibrium characteristics of the atmospheric boundary layer. As a result, the SFE-MEP model significantly improves the performance of SFE ET estimation, particularly for arid regions. The proposed model and its high accuracy in ET estimation enable novel insight into various Earth system models as it does not require any empirical parameters and only uses readily obtainable meteorological variables including reference height air temperature, relative humidity, available energy, and radiometric surface temperature.