Conterminous United States Research Articles

The differential contribution of coyotes and passerines on future biotic carbon storage through juniper seed dispersal

Differences in seed dispersal patterns can alter plant distributions, species persistence, plant community composition, and biotic carbon sequestered within a landscape. Though carnivorans are known to be frugivorous, their contribution to seed dispersal is marginally studied, especially compared to other sympatric dispersers such as passerines. This gap is important to understand because carnivorans may be better suited to assist plant dispersal in disturbed habitats and the longer distances necessary for climate migration. In this study, we evaluated how seed dispersal by a particular carnivoran, coyote Canis latrans, differed from passerines Passeriformes for juniper Juniperus sp. in the conterminous United States under future climate change. We modeled changes in juniper niche suitability starting in 2021 through the next 80 years of climate change by estimating the current niche with Maxent, and then using climate predictions to define spatial changes in suitable niches. Seed dispersal by both coyote and passerine dispersers was simulated to estimate total juniper dispersal, juniper encroachment into grasslands, and finally changes in above‐ground biotic carbon storage due to juniper encroachment. Our models indicate that over the next 80 years, suitable conditions for juniper will contract, but losses from the current range will be minimal. Our model suggests that coyote dispersal of juniper will result in a 54–59% increase in range, which is 2.5 times as much as the estimated increase provided by passerines. We estimate that coyotes will facilitate juniper encroachment into 170 000–185 000 km2 of current grasslands, 3.4 times as much as passerines. Modeling the effect of coyote‐mediated juniper encroachment of grasslands, we forecast that the addition of woody aboveground biomass will provide between 1.1 and 1.2 Pg of additional carbon storage over the next 80 years. Results highlight how coyotes and passerines provide different outcomes for changes in juniper ranges, plant community composition, and landscape carbon storage. Understanding the differences in outcomes provided by different seed dispersers is important for modeling plant species distributions and carbon storage.

Climate-limited vegetation change in the conterminous United States of America.

The effects of climate change on vegetation composition and distribution are evident in different ecosystems around the world. Although some climate-derived alterations on vegetation are expected to result in changes in lifeform fractional cover, disentangling the direct effects of climate change from different non-climate factors, such as land-use change, is challenging. By applying "Liebig's law of the minimum" in a geospatial context, we determined the climate-limited potential for tree, shrub, herbaceous, and non-vegetation fractional cover change for the conterminous United States and compared these potential rates to observed change rates for the period 1986 to 2018. We found that 10% of the land area of the conterminous United States appears to have climate limitations on the change in fractional cover, with a high proportion of these sites located in arid and semiarid ecosystems in the Southwest part of the country. The rates of change in lifeform fractional cover for the remaining area of the country are likely limited by non-climate factors such as the disturbance regime, land management, land-use history, soil conditions, and species interactions and adaptations.

Wildfire CO2 Emissions in the Conterminous United States from 2015 to 2018 as Estimated by the WRF-Chem Assimilation System from OCO-2 XCO2 Retrievals

Wildfires are becoming more frequent due to the global climate change. Large amounts of greenhouse gases emitted by wildfires can lead to increases in extreme climate events. Accurately estimating the greenhouse gas carbon dioxide (CO2) emissions from wildfires is important for mitigation of climate change. In this paper, we develop a novel method to estimate wildfire CO2 emissions from the relationship between local CO2 emissions and XCO2 anomalies. Our method uses the WRF-Chem assimilation system from OCO-2 XCO2 retrievals which coupled with Data Assimilation Research Testbed (DART). To validate our results, we conducted three experiments evaluating the wildfire CO2 emissions over the conterminous United States. The four-month average wildfire emissions from July to October in 2015∼2018 were estimated at 4.408 Tg C, 1.784 Tg C, 1.514 Tg C and 2.873 Tg C, respectively. Compared to the average of established inventories CT2019B, FINNv1.5 and GFASv1.2 fire emissions, our estimates fall within one standard deviation, except for 2017 due to lacking of OCO-2 XCO2 retrievals. These results suggest that the regional carbon assimilation system, such as WRF-Chem/DART, using OCO-2 XCO2 retrievals has a great potential for accurately tracking regional wildfire emissions.

Changes in United States Summer Temperatures Revealed by Explainable Neural Networks

AbstractTo better understand the regional changes in summertime temperatures across the conterminous United States (CONUS), we adopt a recently developed machine learning framework that can be used to reveal the timing of emergence of forced climate signals from the noise of internal climate variability. Specifically, we train an artificial neural network (ANN) on seasonally averaged temperatures across the CONUS and then task the ANN to output the year associated with an individual map. In order to correctly identify the year, the ANN must therefore learn time‐evolving patterns of climate change amidst the noise of internal climate variability. The ANNs are first trained and tested on data from large ensembles and then evaluated using observations from a station‐based data set. To understand how the ANN is making its predictions, we leverage a collection of ad hoc feature attribution methods from explainable artificial intelligence (XAI). We find that anthropogenic signals in seasonal mean minimum temperature have emerged by the early 2000s for the CONUS, which occurred earliest in the Eastern United States. While our observational timing of emergence estimates are not as sensitive to the spatial resolution of the training data, we find a notable improvement in ANN skill using a higher resolution climate model, especially for its early twentieth century predictions. Composites of XAI maps reveal that this improvement is linked to temperatures around higher topography. We find that increases in spatial resolution of the ANN training data may yield benefits for machine learning applications in climate science.

Not just crop or forest: an integrated land cover map for agricultural and natural areas

Due to the key role surrounding landscape plays in ecological processes, a detailed characterization of land cover is critical for researchers and conservation practitioners. Unfortunately, in the United States, land cover data are split across thematic datasets that emphasize agricultural or natural vegetation, but not both. To address this gap, we merged two datasets, the LANDFIRE National Vegetation Classification (NVC) and USDA-NASS Cropland Data Layer (CDL), to produce integrated ‘Spatial Products for Agriculture and Nature’ (SPAN). Our workflow leveraged strengths of the NVC and the CDL to create detailed rasters comprising both agricultural and natural land-cover classes. We generated SPAN annually from 2012–2021 for the conterminous United States, quantified agreement and accuracy of SPAN, and published the complete computational workflow. In our validation analyses, we found that approximately 5.5% of NVC agricultural pixels conflicted with the CDL, but we resolved most conflicts, leaving only 0.6% of agricultural pixels unresolved in SPAN. These ready-to-use rasters characterizing both agricultural and natural land cover will be widely useful in environmental research and management.

GPROF V7 and beyond: assessment of current and potential future versions of the GPROF passive microwave precipitation retrievals against ground radar measurements over the continental US and the Pacific Ocean

Abstract. The Goddard Profiling Algorithm (GPROF) is used operationally for the retrieval of surface precipitation and hydrometeor profiles from the passive microwave (PMW) observations of the Global Precipitation Measurement (GPM) mission. Recent updates have led to GPROF V7, which has entered operational use in May 2022. In parallel, development is underway to improve the retrieval by transitioning to a neural-network-based algorithm called GPROF-NN. This study validates retrievals of liquid precipitation over snow-free and non-mountainous surfaces from GPROF V7 and multiple configurations of GPROF-NN against ground-based radar measurements over the conterminous United States (CONUS) and the tropical Pacific. GPROF retrievals from the GPM Microwave Imager (GMI) are validated over several years, and their ability to reproduce regional precipitation characteristics and effective resolution is assessed. Moreover, the retrieval accuracy for several other sensors of the constellation is evaluated. The validation of GPROF V7 indicates that the retrieval produces reliable estimates of liquid precipitation over the CONUS. During all four assessed years, annual mean precipitation is within 8 % of gauge-corrected radar measurements. Although biases of up to 25 % are observed over sub-regions of the CONUS and the tropical Pacific, the retrieval reliably reproduces each region's diurnal and seasonal precipitation characteristics. The effective resolution of GPROF V7 is found to be 51 km over the CONUS and 18 km over the tropical Pacific. GPROF V7 also produces robust precipitation estimates for the other sensors of the GPM constellation. The evaluation further shows that the GPROF-NN retrievals have the potential to significantly improve the GPM PMW precipitation retrievals. GPROF-NN 1D, the most basic neural network implementation of GPROF, improves the mean-squared error, mean absolute error, correlation and symmetric mean absolute percentage error of instantaneous precipitation estimates by about 20 % for GPROF GMI while the effective resolution is improved to 31 km over land and 15 km over oceans. The two GPROF-NN retrievals that are based on convolutional neural networks can further improve the accuracy up to the level of the combined radar–radiometer retrievals from the GPM core observatory. However, these retrievals are found to overfit on the viewing geometry at the center of the swath, reducing their overall accuracy to that of GPROF-NN 1D. For the other sensors of the constellation, the GPROF-NN retrievals produce larger biases than GPROF V7 and only GPROF-NN 3D achieves consistent improvements compared to GPROF V7 in terms of the other assessed error metrics. This points to shortcomings in the hydrometeor profiles or radiative transfer simulations used to generate the training data for the other sensors of the GPM constellation as a critical limitation for improving GPM PMW retrievals.

Estimating Lithium Concentrations in Groundwater Used as Drinking Water for the Conterminous United States.

Lithium (Li) concentrations in drinking-water supplies are not regulated in the United States; however, Li is included in the 2022 U.S. Environmental Protection Agency list of unregulated contaminants for monitoring by public water systems. Li is used pharmaceutically to treat bipolar disorder, and studies have linked its occurrence in drinking water to human-health outcomes. An extreme gradient boosting model was developed to estimate geogenic Li in drinking-water supply wells throughout the conterminous United States. The model was trained using Li measurements from ∼13,500 wells and predictor variables related to its natural occurrence in groundwater. The model predicts the probability of Li in four concentration classifications, ≤4 μg/L, >4 to ≤10 μg/L, >10 to ≤30 μg/L, and >30 μg/L. Model predictions were evaluated using wells held out from model training and with new data and have an accuracy of 47-65%. Important predictor variables include average annual precipitation, well depth, and soil geochemistry. Model predictions were mapped at a spatial resolution of 1 km2 and represent well depths associated with public- and private-supply wells. This model was developed by hydrologists and public-health researchers to estimate Li exposure from drinking water and compare to national-scale human-health data for a better understanding of dose-response to low (<30 μg/L) concentrations of Li.

Adaptive Mapping of Design Ground Snow Loads in the Conterminous United States

Adaptive Mapping of Design Ground Snow Loads in the Conterminous United States

Automated In-Season Crop-Type Data Layer Mapping Without Ground Truth for the Conterminous United States Based on Multisource Satellite Imagery

Automated In-Season Crop-Type Data Layer Mapping Without Ground Truth for the Conterminous United States Based on Multisource Satellite Imagery

Effect of temperature on growth, survival, and chronic stress responses of Arctic Grayling juveniles.

Arctic Grayling Thymallus arcticus are Holarctically distributed, with a single native population in the conterminous United States occurring in the Big Hole River, Montana, where water temperatures can fluctuate throughout the year from 8 to 18 °C. A gradual increase in mean water temperature has been reported in this river over the past 20 years due to riparian habitat changes and climate change effects. We hypothesized that exposing Arctic Grayling to higher temperatures would result in lower survival, decreased growth, and increased stress responses. Over a 144-day trial, Arctic Grayling juveniles were subjected to water temperatures ranging from 8-26 °C to measure the effects on growth, survival, gene expression and antioxidant enzyme activity. Fish growth increased with increasing water temperature up to 18 °C, beyond which survival was reduced. Fish did not survive at temperatures above 22 °C. In response to temperatures above 16 °C, a 3-fold and 1.5-fold increase in gene expression was observed for superoxide dismutase (SOD) and glutathione peroxidase (GPx), respectively, but no changes were seen in the ratio of Heat Shock Protein 70 (HSP70) and heat shock protein 90 (HSP90) expression. Enzyme activities of SOD and GPx also rose at temperatures above 16 °C, indicating heightened oxidative stress. Catalase (CAT) gene expression and enzyme activity decreased with rising temperatures, suggesting a preference for the GPx pathway, as GPx could also be providing help with lipid peroxidation. An increase of Thiobarbituric acid reactive substances (TBARS) was also recorded, which corresponded with rising temperatures. Our findings thus underscore the vulnerability of Arctic Grayling to minor changes in water temperature. Further increases in mean water temperature could significantly compromise survival of Arctic Grayling in the Big Hole River.

Panel Review of the USGS 2023 Conterminous U.S. Time-Independent Earthquake Rupture Forecast

ABSTRACT This report documents the assessment by the U.S. Geological Survey (USGS) Earthquake Rupture Forecast (ERF) Review Panel of the draft ERF for the conterminous United States (CONUS-ERF23) proposed for the 2023 update of the National Seismic Hazard Model (NSHM23). Panel members participated with the ERF Development Team in several verification and validation exercises, including spot checks of the hazard estimates at key localities. The ERF23 forecast is substantially different from its predecessor, yielding relative differences in hazard that exceed ±50% in some low-hazard areas. These stem primarily from the new model ingredients—new faults, revised deformation rates, and updated seismicity catalogs—rather than from changes in the modeling methodology. The panel found that the main hazard changes are scientifically justified at the long return periods (≥475 yr) for which NSHM23 is applicable. Based on its evaluation of the model, the panel offered six actionable recommendations for improvements to the draft ERF23 for the western United States and two for the Cascadia subduction zone. All eight recommendations were adopted by the USGS for the revised ERF, as documented by Field et al. (2023). The panel concluded that CONUS-ERF23 represents a significant scientific advance over ERF18 and should be incorporated, after suitable revision, into NSHM23. The panel also considered changes to the CONUS-ERF that cannot be feasibly implemented in NSHM23 but could lead to future improvements. Among these aspirational recommendations, the panel prioritized the development of time-dependent extensions of ERF23 that include models of seismic renewal and clustering. The panel endorsed USGS efforts to extend the NSHM to a national earthquake forecasting enterprise capable of continually updating and disseminating authoritative information about future earthquake occurrence through a well-designed hazard-risk interface. Operational earthquake forecasting will place new and heavy demands on USGS cyberinfrastructure, requiring a more integrated approach to software development and workflow management.

The USGS 2023 Conterminous U.S. Time-Independent Earthquake Rupture Forecast

ABSTRACT We present the 2023 U.S. Geological Survey time-independent earthquake rupture forecast for the conterminous United States, which gives authoritative estimates of the magnitude, location, and time-averaged frequency of potentially damaging earthquakes throughout the region. In addition to updating virtually all model components, a major focus has been to provide a better representation of epistemic uncertainties. For example, we have improved the representation of multifault ruptures, both in terms of allowing more and less fault connectivity than in the previous models, and in sweeping over a broader range of viable models. An unprecedented level of diagnostic information has been provided for assessing the model, and the development was overseen by a 19-member participatory review panel. Although we believe the new model embodies significant improvements and represents the best available science, we also discuss potential model limitations, including the applicability of logic tree branch weights with respect different types of hazard and risk metrics. Future improvements are also discussed, with deformation model enhancements being particularly worthy of pursuit, as well as better representation of sampling errors in the gridded seismicity components. We also plan to add time-dependent components, and assess implications with a wider range of hazard and risk metrics.

Mapping multiscale breeding bird species distributions across the United States and evaluating their conservation applications.

Species distribution models are vital to management decisions that require understanding habitat use patterns, particularly for species of conservation concern. However, the production of distribution maps for individual species is often hampered by data scarcity, and existing species maps are rarely spatially validated due to limited occurrence data. Furthermore, community-level maps based on stacked species distribution models lack important community assemblage information (e.g., competitive exclusion) relevant to conservation. Thus, multispecies, guild, or community models are often used in conservation practice instead. To address these limitations, we aimed to generate fine-scale, spatially continuous, nationwide maps for species represented in the North American Breeding Bird Survey (BBS) between 1992 and 2019. We developed ensemble models for each species at three spatial resolutions-0.5, 2.5, and 5 km-across the conterminous United States. We also compared species richness patterns from stacked single-species models with those of 19 functional guilds developed using the same data to assess the similarity between predictions. We successfully modeled 192 bird species at 5-km resolution, 160 species at 2.5-km resolution, and 80 species at 0.5-km resolution. However, the species we could model represent only 28%-56% of species found in the conterminous US BBSs across resolutions owing to data limitations. We found that stacked maps and guild maps generally had high correlations across resolutions (median = 84%), but spatial agreement varied regionally by resolution and was most pronounced between the East and West at the 5-km resolution. The spatial differences between our stacked maps and guild maps illustrate the importance of spatial validation in conservation planning. Overall, our species maps are useful for single-species conservation and can support fine-scale decision-making across the United States and support community-level conservation when used in tandem with guild maps. However, there remain data scarcity issues for many species of conservation concern when using the BBS for single-species models.

The distribution of depth, volume, and basin shape for lakes in the conterminous United States

AbstractDepth regulates many attributes of aquatic ecosystems, but relatively few lakes are measured, and existing datasets are biased toward large lakes. To address this, we used a large dataset of maximum (Zmax; n = 16,831) and mean (Zmean; n = 5,881) depth observations to create new depth models, focusing on lakes &lt; 1,000 ha. We then used the models to characterize patterns in lake basin shape and volume. We included terrain metrics, water temperature and reflectance, polygon attributes, and other predictors in a random forest model. Our final models generally outperformed existing models (Zmax ; root mean square error [RMSE] = 8.0 m and Zmean ; RMSE = 3.0 m). Our models show that lake depth followed a Pareto distribution, with 2.8 orders of magnitude fewer lakes for an order of magnitude increase in depth. In addition, despite orders of magnitude variation in surface area, most size classes had a modal maximum depth of ~ 5 m. Concave (bowl‐shaped) lake basins represented 79% of all lakes, but lakes were more convex (funnel‐shaped) as surface area increased. Across the conterminous United States, 9.8% of all lake water was within the top meter of the water column, and 48% in the top 10 m. Excluding the Laurentian Great Lakes, we estimate the total volume in the conterminous United States is 1,057–1,294 km3, depending on whether Zmax or Zmean was modeled. Lake volume also exhibited substantial geographic variation, with high volumes in the upper Midwest, Northeast, and Florida and low volumes in the southwestern United States.

Yesterday's “lake” endures in its name—The etymology of lake names

AbstractThe global language of limnology is English, but most of our study objects do not have English names. Here, I compare 57,000 lake names in a lake‐rich, non‐English speaking country, that is, Sweden, with a previous analysis of 83,000 lakes in the conterminous United States. The diversity of lake name appellations is strikingly different. In the United States, three different appellations (“lake,” “pond,” “reservoir”) apply to 96% of the lakes, whereas in Sweden to account for 93% of the lakes, 76 different appellations and suffixes were required. The etymology of the remaining largely idiosyncratic 4000 lake names is difficult to assess, and of ancient origin. In the United States, lake names with appellations in languages of non‐English European colonizers are rare and lakes that include words in indigenous languages almost exclusively also include an English appellation. Contrastingly, in regions of Sweden where Sami, Finnish, and Meänkieli are spoken, lake names are typically fully indigenous, including the appellation. The historical reasons for the differences are discussed. Examples of malpractice in the use of lake names in scientific papers are presented, and suggestions are made for how we better can achieve a good lake‐name practice.

An environmental analysis of public UAP sightings and sky view potential

Sightings of unidentified flying objects (UFOs) or unidentified anomalous phenomena (UAP) have been reported throughout history. Given the potential security and safety risks they pose, as well as scientific curiosity, there is increasing interest in understanding what these sighting reports represent. We approach this problem as an important one of the human experience and that can be examined through a geographical lens: what local factors may increase or decrease the number of sighting reports? Using a Bayesian regression method, we test hypotheses based on variables representing sky view potential (light pollution, tree canopy, and cloud cover) and the potential for objects to be present in the sky (aircraft and military installations). The dependent variable includes over 98,000 publicly reported UAP sightings in the conterminous United States during the 20-year period from 2001 to 2020. The model results find credible correlations between variables that suggest people see more “phenomena” when they have more opportunity to. This analysis is one of few investigations of UAP sighting reports at a national scale providing context to help examine individual reports. Given that these objects are labeled unidentifiable in the personal sense, there are many natural and/or human based explanations worth exploring.

Accuracy evaluation and effect factor analysis of GEDI aboveground biomass product for temperate forests in the conterminous United States

ABSTRACT The Global Ecosystem Dynamics Investigation (GEDI) is expected to revolutionize the quantification of aboveground carbon at continental scales, through its unprecedented dense vertical observations of vegetation structure. As its primary task, GEDI recently introduced GEDI L4A, the 25 m near-global footprint aboveground biomass density (AGBD) product. As a global mission with significant policy and management applications, it is urgent to conduct a comprehensive evaluation of GEDI L4A and to analyze the factors affecting the product’s performance. In this study, the accuracy of GEDI L4A is assessed using co-registered airborne Lidar surveys collected during 2018 ~ 2019 and corresponding AGBD plots at 19 sites of the National Ecological Observatory Network (NEON). The analysis included 11 forest types and spanned 17 eco-climatic domains across the conterminous United States to ensure the representativeness and comprehensiveness of the evaluation result. The interplay of nine factors affecting GEDI L4A is quantified, including the simulated waveform strategy deviation (SWSD) used in GEDI L4A, canopy characteristics (tree height, crown size, and canopy cover), canopy heterogeneity (crown size standard deviation, tree height standard deviation, and tree density), and other factors (forest type and topographic slope). Results show that compared with NEON observations, GEDI L4A generally underestimates the AGBD (Bias: −31.65 Mg/ha), with a moderate relative error exhibited in 14 of 19 sites (%RMSE ranging from 19% to 50%). For half of the forest types, the threshold of the lowest accuracy requirement of AGBD products set by GCOS was met or was close to being met. Broadleaf forests with high AGBD values had the lowest %RMSE (less than 35%), while coniferous forests with low AGBD values had the highest %RMSE (over 50%). Among the different factors considered, the SWSD contributed the most to GEDI L4A’s accuracy, with a relative importance of 56.63%, and manifested the indirect impacts of canopy heterogeneity and canopy characteristics. The relative importance of canopy heterogeneity (32.40%) was the second highest after SWSD; it was also much higher than that of canopy characteristics (3.99%). These results indicate the limitation of using only relative heights as predictors in GEDI L4A due to limited representation of horizontal structure and vertical tree complexity within a footprint. The findings in this study are a step forward in GEDI L4A’s appropriate application and provide perspectives to aid its improvement.

Conceptual and Methodological Arguments against the Use of Location Quotient as an Area-Based Measure of Residential Segregation: A Measurement Perspective

Among a wide range of practical applications, the location quotient (LQ) has been used as an area-based measure of residential segregation by race/ethnicity in some studies. However, it does not correspond to any of the five dimensions of residential segregation. Rather, an application of LQ in demographic data analyses brings about an atypical way to quantify the population composition of areal units by race/ethnicity. To clarify misconceptions, the purpose of this study was to demonstrate the relationships between proportions, percentages, and LQs of six racial/ethnic groups in the conterminous United States (US). Since populations change over time, demographic data on race and ethnicity were obtained from the 2000, 2010, and 2020 Census through the US Census Bureau’s website. Using census tracts and counties as the units of analysis, a sequence of scatterplots and associated Pearson’s correlation coefficients (r) was used to display the analytical results of census-tract- and county-based measures at three different time periods. Despite the different levels of aggregation, the relationships between proportions, percentages, and LQs of six racial/ethnic groups consistently showed perfect positive correlations at three different time periods (r = 1.00). These suggest that census-tract- and county-based measures expressed as the proportion, percentage, and LQ of a racial/ethnic group capture the same distributional pattern, but the units of measurement simply differ from one another. Hence, the study of residential segregation and its societal consequences needs to be specific to the dimension under study and to build upon the conceptual and methodological foundations established by sociologists-demographers and geographers.

Evaluation of gridded precipitation data in water availability modeling in CONUS

Water availability estimates are affected by the quality of data used. In this study precipitation data from meteorological forcing datasets specifically compiled for large-scale hydrological modeling are evaluated over the Conterminous United States (CONUS). The performance of the datasets was evaluated using Pearson Correlation (r), Nash Sutcliff Efficiency (NSE), Percent Bias (PBIAS), and Kling Gupta Efficiency (KGE) criterion. Three observation-based gridded precipitation data from the Inter-Sectoral Impact Model Intercomparison Project – ISIMIP (EartH2Observe, WFDEI and ERA-Interim data Merged and Bias-corrected for ISIMIP (EWEMBI), WATCH Forcing Data methodologyapplied to ERA-Interim data (WFDEI), and WFDE5 over land merged with ERA5 over the ocean (W5E5)) and one regional dataset (GRIDMET) were compared. The datasets were compared using more than 3300 gage precipitation observations. For grid-level comparisons, the Climate Prediction Center's Unified gage-based gridded precipitation dataset (CPC) was used as a reference dataset after verification with gage observations. The grid-to-grid comparison was made on daily, monthly, annual, and seasonal time steps. Grid runoff and discharge were simulated using the Community Water Model (CWatM) model. The flow estimations using the datasets were compared with CPC estimations and with more than 900 discharge observations distributed over the nine climate regions of CONUS. Furthermore, the per capita water availability was estimated and compared. In comparison with gage precipitation observation, CPC had superior performance, followed by GRIDMET. CPC resulted in daily KGE, NSE, and r above 0.5, and PBIAS within 25 % in 79 %, 69 %, 87 %, and 90 % of the stations, respectively. Grid-to-grid precipitation comparisons resulted in overall comparable performance between the datasets. In grid discharge estimation, GRIDMET (median KGE = 0.68) had the highest correlation with CPC. WFDEI, W5E5, and EWEMBI show close performance to each other in estimating grid discharge with median KGE values of 0.43, 0.25, and 0.45 respectively. The performance of the datasets as compared to observed stream flow gages also showed comparable performance with above zero KGE in 73, 56, 71, 60, and 70 stations by GRIDMET, EWEMBI, W5E5, and WFDEI, and CPC respectively. In estimating water availability per capita, W5E5 showed an overestimation as compared to ISMIP datasets. The estimations from CPC and GRIDMET were close to each other. This study serves as a valuable resource for guiding the selection of a suitable dataset for large-scale modeling over the CONUS. By providing performance information for the compared datasets, it assists in making informed data selection and contributes to our understanding of how the selection of input datasets affect water availability modeling.

Historical evaluation and future projections of compound heatwave and drought extremes over the conterminous United States in CMIP6 * *Notice: This manuscript has been authored by employees of UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). Accordingly, the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide

Independently, both droughts and heatwaves can induce severe impacts on human and natural systems. However, when these two climate extremes occur concurrently in a given region, their compound impacts are often more pronounced. With the improvement in both the spatiotemporal resolution and representation of complex climate processes in the global climate models (GCMs), they are increasingly used to study future changes in these extremes and associated regional impacts. However, GCM selection for such impact assessments is generally based on historical performance and/or future mean changes, without considering individual or compound extremes. In contrast, this study evaluates historical performance and projected changes in heatwaves, droughts, and compound heatwave-droughts using an ensemble of GCMs from the latest Phase 6 of Coupled Models Intercomparison Project at a regional scale across the conterminous United States. Additionally, we explore the inter-model differences in the projected changes that are associated with various characteristics of extremes and the choice of drought indices. Our analysis reveals considerable variation among the GCMs, as well as substantial differences in the projected changes based on the choice of drought indices and region of interest. For example, the projected increases in both the frequency and intensity of drought and associated compound extreme days, based on the standardized precipitation evapotranspiration index far exceed those derived from the standard precipitation index. Further, the largest changes in the frequency of compound extremes are projected over the Southwest, South Central, and parts of the Southeast while the smallest changes are projected over the Northeast. Overall, this study provides important insights for the interpretation and selection of GCMs for future assessment studies that are crucial for the development of regional adaptation strategies in the face of climate change.