Fat, weather, and date affect migratory songbirds' departure decisions, routes, and time it takes to cross the Gulf of Mexico.

Approximately two thirds of migratory songbirds in eastern North America negotiate the Gulf of Mexico (GOM), where inclement weather coupled with no refueling or resting opportunities can be lethal. However, decisions made when navigating such features and their consequences remain largely unknown due to technological limitations of tracking small animals over large areas. We used automated radio telemetry to track three songbird species (Red-eyed Vireo, Swainson's Thrush, Wood Thrush) from coastal Alabama to the northern Yucatan Peninsula (YP) during fall migration. Detecting songbirds after crossing ∼1,000 km of open water allowed us to examine intrinsic (age, wing length, fat) and extrinsic (weather, date) variables shaping departure decisions, arrival at the YP, and crossing times. Large fat reserves and low humidity, indicative of beneficial synoptic weather patterns, favored southward departure across the Gulf. Individuals detected in the YP departed with large fat reserves and later in the fall with profitable winds, and flight durations (mean = 22.4 h) were positively related to wind profit. Age was not related to departure behavior, arrival, or travel time. However, vireos negotiated the GOM differently than thrushes, including different departure decisions, lower probability of detection in the YP, and longer crossing times. Defense of winter territories by thrushes but not vireos and species-specific foraging habits may explain the divergent migratory behaviors. Fat reserves appear extremely important to departure decisions and arrival in the YP. As habitat along the GOM is degraded, birds may be limited in their ability to acquire fat to cross the Gulf.

Urban water and electricity demand data for understanding climate change impacts on the water-energy nexus.

As the climate crisis intensifies, it is becoming increasingly important to conduct research aimed at fully understanding the climate change impacts on various infrastructure systems. In particular, the water-electricity demand nexus is a growing area of focus. However, research on the water-electricity demand nexus requires the use of demand data, which can be difficult to obtain, especially across large spatial extents. Here, we present a dataset containing over a decade (2007-2018) of monthly water and electricity consumption data for 46 major US cities (2018 population >250,000). Additionally, we include pre-processed climate data from the North American Regional Reanalysis (NARR) to supplement studies on the relationship between the water-electricity demand nexus and the local climate. This data can be used for a number of studies that require water and/or electricity demand data across long time frames and large spatial extents. The data can also be used to evaluate the possible impacts of climate change on the water-electricity demand nexus by leveraging the relationship between the observed values.

Predicting Urban Reservoir Levels Using Statistical Learning Techniques.

Urban water supplies are critical to the growth of the city and the wellbeing of its citizens. However, these supplies can be vulnerable to hydrological extremes, such as droughts and floods, especially if they are the main source of water for the city. Maintaining these supplies and preparing for future conditions is a crucial task for water managers, but predicting hydrological extremes is a challenge. This study tested the abilities of eight statistical learning techniques to predict reservoir levels, given the current hydroclimatic conditions, and provide inferences on the key predictors of reservoir levels. The results showed that random forest, an ensemble, tree-based method, was the best algorithm for predicting reservoir levels. We initially developed the models using Lake Sidney Lanier (Atlanta, Georgia) as the test site; however, further analysis demonstrated that the model based on the random forest algorithm was transferable to other reservoirs, specifically Eagle Creek (Indianapolis, Indiana) and Lake Travis (Austin, Texas). Additionally, we found that although each reservoir was impacted differently, streamflow, city population, and El Niño/Southern Oscillation (ENSO) index were repeatedly among the most important predictors. These are critical variables which can be used by water managers to recognize the potential for reservoir level changes.

Droughts in India from 1981 to 2013 and Implications to Wheat Production.

Understanding drought from multiple perspectives is critical due to its complex interactions with crop production, especially in India. However, most studies only provide singular view of drought and lack the integration with specific crop phenology. In this study, four time series of monthly meteorological, hydrological, soil moisture, and vegetation droughts from 1981 to 2013 were reconstructed for the first time. The wheat growth season (from October to April) was particularly analyzed. In this study, not only the most severe and widespread droughts were identified, but their spatial-temporal distributions were also analyzed alone and concurrently. The relationship and evolutionary process among these four types of droughts were also quantified. The role that the Green Revolution played in drought evolution was also studied. Additionally, the trends of drought duration, frequency, extent, and severity were obtained. Finally, the relationship between crop yield anomalies and all four kinds of drought during the wheat growing season was established. These results provide the knowledge of the most influential drought type, conjunction, spatial-temporal distributions and variations for wheat production in India. This study demonstrates a novel approach to study drought from multiple views and integrate it with crop growth, thus providing valuable guidance for local drought mitigation.