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Abstract. Land surface temperature (LST) is one of the most important and widely used parameters for studying land surface processes. Moderate ResolutionImaging Spectroradiometer (MODIS) LST products (e.g., MOD11A1 and MYD11A1) can provide this information with moderate spatiotemporal resolution withglobal coverage. However, the applications of these data are hampered because of missing values caused by factors such as cloud contamination,indicating the necessity to produce a seamless global MODIS-like LST dataset, which is still not available. In this study, we used a spatiotemporalgap-filling framework to generate a seamless global 1 km daily (mid-daytime and mid-nighttime) MODIS-like LST dataset from 2003 to 2020based on standard MODIS LST products. The method includes two steps: (1) data pre-processing and (2) spatiotemporal fitting. In the datapre-processing, we filtered pixels with low data quality and filled gaps using the observed LST at another three time points of the same day. In thespatiotemporal fitting, first we fitted the temporal trend (overall mean) of observations based on the day of year (independent variable) in eachpixel using the smoothing spline function. Then we spatiotemporally interpolated residuals between observations and overall mean values for eachday. Finally, we estimated missing values of LST by adding the overall mean and interpolated residuals. The results show that the missing values inthe original MODIS LST were effectively and efficiently filled with reduced computational cost, and there is no obvious block effect caused by largeareas of missing values, especially near the boundary of tiles, which might exist in other seamless LST datasets. The cross-validation withdifferent missing rates at the global scale indicates that the gap-filled LST data have high accuracies with the average root mean squared error(RMSE) of 1.88 and 1.33∘, respectively, for mid-daytime (13:30) and mid-nighttime (01:30). The seamless global daily (mid-daytime andmid-nighttime) LST dataset at a 1 km spatial resolution is of great use in global studies of urban systems, climate research and modeling,and terrestrial ecosystem studies. The data are available at Iowa State University's DataShare at https://doi.org/10.25380/iastate.c.5078492 (T. Zhanget al., 2021). 

Abstract Artificial light at night (ALAN), an increasing anthropogenic driver, is widespread and shows rapid expansion with potential adverse impact on the terrestrial ecosystem. However, whether and to what extent does ALAN affect plant phenology, a critical factor influencing the timing of terrestrial ecosystem processes, remains unexplored due to limited ALAN observation. Here, we used the Black Marble ALAN product and phenology observations from USA National Phenology Network to investigate the impact of ALAN on deciduous woody plants phenology in the conterminous United States. We found that (1) ALAN significantly advanced the date of breaking leaf buds by 8.9 ± 6.9 days (mean ± SD) and delayed the coloring of leaves by 6.0 ± 11.9 days on average; (2) the magnitude of phenological changes was significantly correlated with the intensity of ALAN (P < 0.001); and (3) there was an interaction between ALAN and temperature on the coloring of leaves, but not on breaking leaf buds. We further showed that under future climate warming scenarios, ALAN will accelerate the advance in breaking leaf buds but exert a more complex effect on the coloring of leaves. This study suggests intensified ALAN may have far-reaching but underappreciated consequences in disrupting key ecosystem functions and services, which requires an interdisciplinary approach to investigate. Developing lighting strategies that minimize the impact of ALAN on ecosystems, especially those embedded and surrounding major cities, is challenging but must be pursued. 

Insufficiently calibrated forest parameters of the Soil & Water Assessment Tool (SWAT) may introduce uncertainties to water resource projections in forested watersheds. In this study, we improved SWAT forest parameterization and phosphorus cycling representations to better simulate forest ecosystems in the St. Croix River basin, and we further examined how those improvements affected model projections of streamflow, sediment, and nitrogen export under future climate conditions. Simulations with improved forest parameters substantially reduced model estimates of water, sediment, and nitrogen fluxes relative to those based on default parameters. Differences between improved and default projections can be attributed to the enhanced representation of forest water consumption, nutrient uptake, and protection of soil from erosion. Better representation of forest ecosystems in SWAT contributes to constraining uncertainties in water resource projections. Results of this study highlight the importance of improving SWAT forest ecosystem representations in projecting delivery of water, sediment, and nutrients from land to rivers in response to climate change, particularly for watersheds with large areas of forests. Improved forest parameters and the phosphorus weathering algorithms developed in this study are expected to help enhance future applications of SWAT to investigate hydrological and biogeochemical consequences of climate change.