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1. Nonlinear Seasonal and Long-Term Trends in a Twentieth-Century Meteorological Drought Index across the Continental United States

   https://doi.org/10.1175/JCLI-D-22-0045.1  

   Sung, Kyungmin; Stagge, James H. (September 2022, Journal of Climate)

   Abstract Analyzing gradual trends in meteorological drought has become increasingly important as anthropogenic climate change and natural climate variability interact to complicate measurement of drought severity. Complex seasonality and long-term trends pose a limitation in understanding spatial trends in nonstationary changes of meteorological drought in the United States. This study seeks to address this issue by simultaneously analyzing recurring seasonal patterns (stationary component) and long-term drought trends (nonstationary component), with a unique focus on nonlinear trends and common regional patterns. We analyzed 696 instrumental precipitation gauges with long historical records in the continental United States, using a novel spline-based model to disaggregate a 3-month meteorological drought index (SPI) into its seasonal and long-term components. The disaggregated components for each gauge were then clustered into subregions with similar seasonality and groupings with similar long-term trends using a two-step process. Our results identify clearly defined regions based on precipitation seasonality, while long-term trends are not spatially coherent with the seasonality. Instead, these findings support prior findings of an increasingly drier western United States and an increasingly wetter eastern United States over the last century, but with more nuanced spatial and temporal patterns. The new clustering analysis based on nonstationary meteorological drought trends can contribute to informing and adapting current water management strategies to long-term drought trends. Significance Statement This study considered 656 precipitation gauges across the continental United States to find regions with similar precipitation seasonality and then to group records with similar long-term climate trends. The study focused on 3-month average precipitation, a key indicator for drought monitoring. We identified eight regions across the United States with similar precipitation seasonality. From 1920 to the present, we found continuous drying trends throughout the western United States, continuously wetter trends in the northern plains, and an overall wetter trend interrupted by a midcentury dry period (1930–50) for much of the central Plains and Midwest. This study’s use of splines, or fitted curves, allowed these nonlinear patterns, which we believe better capture the nuances and intensification of climate change effects on precipitation. 

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2. Changes in monthly baseflow across the U.S. Midwest

   https://doi.org/10.1002/hyp.13359  

   Ayers, Jessica R.; Villarini, Gabriele; Jones, Christopher; Schilling, Keith (December 2018, Hydrological Processes)

   Abstract Characterizing streamflow changes in the agricultural U.S. Midwest is critical for effective planning and management of water resources throughout the region. The objective of this study is to determine if and how baseflow has responded to land alteration and climate changes across the study area during the 50‐year study period by exploring hydrologic variations based on long‐term stream gage data. This study evaluates monthly contributions to annual baseflow along with possible trends over the 1966–2016 period for 458 U.S. Geological Survey streamflow gages within 12 different Midwestern states. It also examines the influence of climate and land use factors on the observed baseflow trends. Monthly contribution breakdowns demonstrate how the majority of baseflow is discharged into streams during the spring months (March, April, and May) and is overall more substantial throughout the spring (especially in April) and summer (June, July, and August). Baseflow has not remained constant over the study period, and the results of the trend detection from the Mann–Kendall test reveal that baseflows have increased and are the strongest from May to September. This analysis is confirmed by quantile regression, which suggests that for most of the year, the largest changes are detected in the central part of the distribution. Although increasing baseflow trends are widespread throughout the region, decreasing trends are few and limited to Kansas and Nebraska. Further analysis reveals that baseflow changes are being driven by both climate and land use change across the region. Increasing trends in baseflow are linked to increases in precipitation throughout the year and are most prominent during May and June. Changes in agricultural intensity (in terms of harvested corn and soybean acreage) are linked to increasing trends in the central and western Midwest, whereas increasing temperatures may lead to decreasing baseflow trends in spring and summer in northern Wisconsin, Kansas, and Nebraska. 

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3. A Framework for Assessing the Habitat Correlates of Spatially Explicit Population Trends

   https://doi.org/10.1111/ddi.70030  

   Stillman, Andrew N; Davis, Courtney L; Dunham, Kylee D; Ruiz‐Gutierrez, Viviana; Rodewald, Amanda D; Johnston, Alison; Auer, Tom; Strimas‐Mackey, Matt; Ligocki, Shawn; Fink, Daniel (May 2025, Diversity and Distributions)

   ABSTRACT AimHalting widespread biodiversity loss will require detailed information on species' trends and the habitat conditions correlated with population declines. However, constraints on conventional monitoring programs and commonplace approaches for trend estimation can make it difficult to obtain such information across species' ranges. Here, we demonstrate how recent developments in machine learning and model interpretation, combined with data sources derived from participatory science, enable landscape‐scale inferences on the habitat correlates of population trends across broad spatial extents. LocationWorldwide, with a case study in the western United States. MethodsWe used interpretable machine learning to understand the relationships between land cover and spatially explicit bird population trends. Using a case study with three passerine birds in the western U.S. and spatially explicit trends derived from eBird data, we explore the potential impacts of simulated land cover modification while evaluating potential co‐benefits among species. ResultsOur analysis revealed complex, non‐linear relationships between land cover variables and species' population trends as well as substantial interspecific variation in those relationships. Areas with the most positive impacts from a simulated land cover modification overlapped for two species, but these changes had little effect on the third species. Main ConclusionsThis framework can help conservation practitioners identify important relationships between species trends and habitat while also highlighting areas where potential modifications to the landscape could bring the biggest benefits. The analysis is transferable to hundreds of species worldwide with spatially explicit trend estimates, allowing inference across multiple species at scales that are tractable for management to combat species declines. 

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4. Changes in monthly baseflow across the U.S. Midwest

   Ayers, J.; Villarini, G.; Jones, C.; Schilling, K. (December 2018, Hydrological processes)

   Characterizing streamflow changes in the agricultural U.S. Midwest is critical foreffective planning and management of water resources throughout the region. Theobjective of this study is to determine if and how baseflow has responded to landalteration and climate changes across the study area during the 50‐year study periodby exploring hydrologic variations based on long‐term stream gage data. This studyevaluates monthly contributions to annual baseflow along with possible trends overthe 1966–2016 period for 458 U.S. Geological Survey streamflow gages within 12different Midwestern states. It also examines the influence of climate and land usefactors on the observed baseflow trends. Monthly contribution breakdowns demon-strate how the majority of baseflow is discharged into streams during the springmonths (March, April, and May) and is overall more substantial throughout the spring(especially in April) and summer (June, July, and August). Baseflow has not remainedconstant over the study period, and the results of the trend detection from theMann–Kendall test reveal that baseflows have increased and are the strongest fromMay to September. This analysis is confirmed by quantile regression, which suggeststhat for most of the year, the largest changes are detected in the central part of thedistribution. Although increasing baseflow trends are widespread throughout theregion, decreasing trends are few and limited to Kansas and Nebraska. Furtheranalysis reveals that baseflow changes are being driven by both climate and landuse change across the region. Increasing trends in baseflow are linked to increasesin precipitation throughout the year and are most prominent during May and June.Changes in agricultural intensity (in terms of harvested corn and soybean acreage)are linked to increasing trends in the central and western Midwest, whereasincreasing temperatures may lead to decreasing baseflow trends in spring and summerin northern Wisconsin, Kansas, and Nebraska. 

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5. Observed and projected trends in climate extremes in a tropical highland region: An agroecosystem perspective

   https://doi.org/10.1002/joc.7378  

   Birhan, Dereje Ademe; Zaitchik, Benjamin F.; Fantaye, Kindie Tesfaye; Birhanu, Belay Simane; Damot, Getachew Alemayehu; Tsegaye, Enyew Adgo (October 2021, International Journal of Climatology)

   Abstract Tropical highland environments present substantial challenges for climate projections due to sparse observations, significant local heterogeneity and inconsistent performance of global climate models (GCMs). Moreover, these areas are often densely populated, with agriculture‐based livelihoods sensitive to transient climate extremes not always included in available climate projections. In this context, we present an analysis of observed and projected trends in temperature and precipitation extremes across agroecosystems (AESs) in the northwest Ethiopian Highlands, to provide more relevant information for adaptation. Limited observational networks are supplemented with a satellite‐station hybrid product, and trends are calculated locally and summarized at the adaptation‐relevant unit of the AES. Projections are then presented from GCM realizations with divergent climate projections, and results are interpreted in the context of agricultural climate sensitivities. Trends in temperature extremes (1981–2016) are typically consistent across sites and AES, but with different implications for agricultural activities in the other AES. Trends in temperature extremes from GCM projected data also generally have the same sign as the observed trends. For precipitation extremes, there is greater site‐to‐site variability. Summarized by AES, however, there is a clear tendency towards reduced precipitation, associated with decreases in wet extremes and a tendency towards temporally clustered wet and dry days. Over the retrospective analysis period, neither of the two analysed GCMs captures these trends. Future projections from both GCMs include significant wetting and an increase in precipitation extremes across AES. However, given the lack of agreement between GCMs and observations with respect to trends in recent decades, the reliability of these projections is questionable. The present study is consistent with the “East Africa Paradox” that observations show drying in summer season rainfall while GCMs project wetting. This has an expression in summertime Ethiopian rain that has not received significant attention in previous studies. 

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