Fine Spatiotemporal Resolution Research Articles

A combined extended triple collocation and cumulative distribution function merging framework for improved daily precipitation estimates over mainland China

Accurate monitoring of daily precipitation is essential for many applications, such as simulation of water-related disasters and other hydrometeorology studies. However, precipitation data from satellite and gauge-based precipitation products face challenges in capturing true observations because of their large uncertainties. To improve daily precipitation estimates, this study proposed a new framework, ETCCCDF, that merges multi-source precipitation estimates by combining an extended triple collocation (ETCC) with maximized correlation and cumulative distribution function (CDF) matching with reduced bias. The ETCCCDF framework was applied to systematically integrate three independent precipitation products from SM2RAIN-ASCAT satellite soil moisture estimator, IMERG Early Run satellite retrieval, and CPC unified gauge-based analysis. A new merged precipitation dataset, with a fine spatiotemporal resolution of 0.25°/1 d, was developed and then thoroughly evaluated using 2304 stations over mainland China from 2007 to 2014. The results showed that the ETCCCDF framework significantly improved the precipitation representation in terms of the correlation, bias, and absolute difference, and it increased modified Kling-Gupta efficiency value relative to any of the parent products, demonstrating the effectiveness of the proposed merging framework. In addition, the ETCCCDF framework is more robust than the ETCC, triple collocation (TC) merging, and combined strategy of TC with CDF, in which CDF matching successfully corrects the precipitation biases in collocation fusions. The accuracy of the merged products in heavy rainfall estimation during Typhoon Morakot in 2009 and Typhoon Fitow in 2013 was further evaluated. The results confirmed that the ETCCCDF-merged dataset performed satisfactorily for heavy rainfall events and presented superior statistical scores. This study proves that the ETCCCDF framework is a promising solution for obtaining high-quality precipitation data, particularly for limited-gauge areas.

Repeat GEDI footprints measure the effects of tropical forest disturbances

More of the Amazon rainforest is disturbed each year than completely deforested, but the impact of these disturbances on the carbon cycle remains poorly understood. Recent algorithmic advances using optical and radar remote sensing have improved detection of disturbances at fine spatiotemporal resolution, but quantifying changes in forest structure and biomass associated with these detected disturbances has proven challenging. The Global Ecosystem Dynamics Investigation (GEDI), a spaceborne lidar mission collecting data from 2019 to 2023, provides an opportunity to address this problem. GEDI captured billions of measurements of forest height, plant area, and understory structure within ∼25-m diameter footprints scattered across the tropics. Though the instrument had no guaranteed repeat cycle, it sometimes sampled nearby locations twice; some of these spatially near-coincident footprints happened to measure forest structure before and after a detected disturbance, providing information on how disturbance affected aboveground biomass. In this study, we developed an efficient general-purpose open-source pipeline for identifying spatially coincident footprints, which is a computationally complex task, and used the pipeline to find over 13,700 footprint pairs with intervening disturbance events across the Amazon biome. We also identified a set of ∼65,000 spatially near-coincident footprint pairs that lacked an intervening disturbance but came from regions with similar disturbance threat; these provided a control dataset to evaluate the effectiveness of estimating forest structure and biomass changes with nearby footprints. Analysis of this Amazon-wide dataset demonstrated that GEDI was able to measure statistically significant canopy height and biomass deficits following non-stand-replacing disturbances as small as 900 m2 (30 m × 30 m). GEDI's unique three-dimensional view of forest structure also reflected the effects of different intensities of fire disturbance, including 20% of burned areas where the upper canopy retained most of its height, but the understory suffered substantial foliage losses. Finally, we modeled the relationship between Landsat and Sentinel-1 disturbance detection parameters and GEDI-estimated percent biomass loss, showing that certain satellite-derived intensity metrics are correlated with increasing biomass loss and identifying temporal trends in biomass loss and recovery following disturbance. This work represents an important step towards the development of a pan-tropical, spatially explicit system for tracking carbon losses and structural changes arising from forest disturbance.

Automated and repeated aerial observations of GPS‐collared animals using UAVs and open‐source electronics

AbstractTelemetry has enabled ecologists to link animal movement trajectories and environmental features at a fine spatiotemporal resolution; however, the effects of social interactions on individual choice within large mobile groups remain largely unknown. Estimating the effect of social interaction in the wild remains challenging because existing long‐term tracking tools such as GPS collars focus on the movements of a single individual and cannot observe the behavior of other individuals within the group. The progression of socially informed movement models requires measuring simultaneous trajectories of many individuals at once, as well as the instantaneous social cues to which individuals may be responding. The availability of low‐flying unmanned aerial vehicles (UAVs) and low‐cost open‐source electronics presents a promising opportunity to collect fine‐scale data on social interactions in order to advance our understanding of collective behavior. Here, we present a tracking system that enables the repeated localization and observation of a collared individual and its near neighbors using nadir video footage collected from a commercial UAV. We make use of open‐source electronics combined with the UAV's in‐built functionality that allows it to follow a stream of GPS locations to create an automated system that can follow a specific individual without user control. We demonstrate the tracking systems' performance by studying the group movements of a herd of Exmoor ponies (Equus ferus caballus), and as a proof of concept, we examine the position of the focal individual (collared animal) in relation to the center of the video frame. We also collect information about the focal individual's nearest neighbors. The automated animal observation tool is effective at consistently keeping the focal individual close to the center of the video frame, offering a new dimension to existing remote telemetry tools. For instance, the repeated observation of the same individual in different physiological states, seasons, and demographic groups potentially opens new avenues in collective movement ecology research. By making our design, software, and firmware freely available, we aim to encourage continuous improvements to collective behavior research and to facilitate replicable approaches across other species and ecosystems.

Statistical evaluation of satellite-based CHIRPS precipitation data averaged over the midland and highland regions of Kidangoor sub-catchment, Kerala

The steep topographical setting of Kerala, traversing from Western Ghats in the east to the sandy beaches on the west, demands the use of precipitation data at a very fine spatio-temporal resolution for a range of hydrological and hydrometeorological studies. The limitation of the existing rain gauge network data in representing the variability in the monsoon showers received, across the physiographic divisions of the state, could be overcome using satellite rainfall dataset offered at a finer resolution. In this paper, a statistical evaluation of the satellite derived CHIRPS (Climate Hazards Group Infrared Precipitation with Stations) precipitation data for the Kidangoor sub-catchment was performed by comparing it with station rainfall data and IMD gridded data sets. The homogeneity test at 95 % confidence level classified the station data under ‘useful’ category. Additionally, the statistical performance matrices suggested that the CHIRPS data slightly underestimated the observed station rainfall data. However, the coefficient of determination R2 values (0.95-0.97) in the monthly series and (0.37 - 0.64) in the annual series demonstrated a strong to moderate positive correlation between the datasets. To summarize, the quantitative statistical performance matrices, evaluated for the first time in the study area, proposed that the CHIRPS rainfall estimates could very well reproduce the ground-based monthly rainfall datasets and could also serve as a good replacement for IMD gridded data.

Temporal variables improve a spatiotemporal species distribution model for the non-native freshwater fish Candidia temminckii

Ecosystem conservation requires a deeper understanding of species-habitat relationships and population dynamics at a fine spatiotemporal resolution. We propose a new distribution modeling method based on a 5-year monthly survey that considers the temporal continuity of species distributions and physical habitat datasets by inputting continuous time-related variables. We employed random forests to relate the presence/absence of the non-native freshwater fish Candidia temminckii to physical habitat data at 15 sampling sites along a 1.4km spring-fed river in Japan. The proposed method outperforms all conventional methods using datasets split into a specific time period to incorporate temporality into the model. The order of variable importance and shape of the partial dependence plots of the proposed method reflect species ecology and show a gradual shift over time compared to the conventional methods. These results demonstrate the applicability of the proposed method to species distribution modeling using fine-scale spatiotemporal data.

Nationwide estimation of daily ambient PM2.5 from 2008 to 2020 at 1 km2 in India using an ensemble approach.

High-resolution assessment of historical levels is essential for assessing the health effects of ambient air pollution in the large Indian population. The diversity of geography, weather patterns, and progressive urbanization, combined with a sparse ground monitoring network makes it challenging to accurately capture the spatiotemporal patterns of ambient fine particulate matter (PM2.5) pollution in India. We developed a model for daily average ambient PM2.5 between 2008 and 2020 based on monitoring data, meteorology, land use, satellite observations, and emissions inventories. Daily average predictions at each 1 km × 1 km grid from each learner were ensembled using a Gaussian process regression with anisotropic smoothing over spatial coordinates, and regression calibration was used to account for exposure error. Cross-validating by leaving monitors out, the ensemble model had an R2 of 0.86 at the daily level in the validation data and outperformed each component learner (by 5-18%). Annual average levels in different zones ranged between 39.7 μg/m3 (interquartile range: 29.8-46.8) in 2008 and 30.4 μg/m3 (interquartile range: 22.7-37.2) in 2020, with a cross-validated (CV)-R2 of 0.94 at the annual level. Overall mean absolute daily errors (MAE) across the 13 years were between 14.4 and 25.4 μg/m3. We obtained high spatial accuracy with spatial R2 greater than 90% and spatial MAE ranging between 7.3-16.5 μg/m3 with relatively better performance in urban areas at low and moderate elevation. We have developed an important validated resource for studying PM2.5 at a very fine spatiotemporal resolution, which allows us to study the health effects of PM2.5 across India and to identify areas with exceedingly high levels.

End-to-end 3D CNN for plot-scale soybean yield prediction using multitemporal UAV-based RGB images

Crop yield prediction from UAV images has significant potential in accelerating and revolutionizing crop breeding pipelines. Although convolutional neural networks (CNN) provide easy, accurate and efficient solutions over traditional machine learning models in computer vision applications, a CNN training requires large number of ground truth data, which is often difficult to collect in the agricultural context. The major objective of this study was to develope an end-to-end 3D CNN model for plot-scale soybean yield prediction using multitemporal UAV-based RGB images with approximately 30,000 sample plots. A low-cost UAV-RGB system was utilized and multitemporal images from 13 different experimental fields were collected at Argentina in 2021. Three commonly used 2D CNN architectures (i.e., VGG, ResNet and DenseNet) were transformed into 3D variants to incorporate the temporal data as the third dimension. Additionally, multiple spatiotemporal resolutions were considered as data input and the CNN architectures were trained with different combinations of input shapes. The results reveal that: (a) DenseNet provided the most efficient result (R2 0.69) in terms of accuracy and model complexity, followed by VGG (R2 0.70) and ResNet (R2 0.65); (b) Finer spatiotemporal resolution did not necessarily improve the model performance but increased the model complexity, while the coarser resolution achieved comparable results; and (c) DenseNet showed lower clustering patterns in its prediction maps compared to the other models. This study clearly identifies that multitemporal observation with UAV-based RGB images provides enough information for the 3D CNN architectures to accurately estimate soybean yield non-destructively and efficiently.

Decoding the internesting movements of marine turtles using a fine-scale behavioral state approach

IntroductionAn understanding of animal behavior is critical to determine their ecological role and to inform conservation efforts. However, observing hidden behaviors can be challenging, especially for animals that spend most of their time underwater. Animal-borne devices are valuable tools to estimate hidden behavioral states.MethodsWe investigated the fine-scale behavior of internesting hawksbill turtles using the mixed-membership method for movement (M4) which integrated dive variables with spatial components and estimated latent behavioral states.ResultsFive latent behavioral states were identified: 1) pre-nesting, 2) transit, 3) quiescence, and 4) area restricted search within and 5) near the residence of turtles. The last three states associated with a residency period, showed lower activity levels. Notably, when compared to other behaviors the pre-nesting exhibited shallower and remarkably long dives of up to 292 minutes. We noted high fidelity to residence core areas and nesting beaches, within and between nesting seasons, with residence areas decreasing within a season.DiscussionThe latent behaviors identified provide the most detailed breakdown of turtle movement behaviors during the internesting period to date, providing valuable insights into their ecology and behavior. This information can inform marine turtle conservation and management efforts since utilization distributions of individual behavioral states can be used to determine spatially-explicit susceptibility of turtles to various threats based on their behavior. The analyses of utilization distribution revealed a minimal overlap with existing marine protected areas (0.4%), and we show how a new proposal would expand protection to 30%. In short, this study provides valuable guidance for conservation and management of internesting marine turtles at a fine spatiotemporal resolution and can be used to enhance national action plans for endangered species, including the expansion of existing Marine Protected Areas. By flexibly incorporating biologically informative parameters, this approach can be used to study behavior outside of the hawksbill breeding season or even beyond this species.

Reconstruction of Snow Cover in Kaidu River Basin via Snow Grain Size Gap-Filling Based on Machine Learning

Fine spatiotemporal resolution snow monitoring at the watershed scale is crucial for the management of snow water resources. This research proposes a cloud removal algorithm via snow grain size (SGS) gap-filling based on a space–time extra tree, which aims to address the issue of cloud occlusion that limits the coverage and time resolution of long-time series snow products. To fully characterize the geomorphic characteristics and snow duration time of the Kaidu River Basin (KRB), we designed dimensional data that incorporate spatiotemporal information. Combining other geographic and snow phenological information as input for estimating SGS. A spatiotemporal extreme tree model was constructed and trained to simulate the nonlinear mapping relationship between multidimensional inputs and SGS. The estimation results of SGS can characterize the snow cover under clouds. This study found that when the cloud cover is less than 70%, the model’s estimation of SGS meets expectations, and snow cover reconstruction achieves good results. In specific cloud removal cases, compared to traditional spatiotemporal filtering and multi-sensor fusion, the proposed method has better detail characterization ability and exhibits better performance in snow cover reconstruction and cloud removal in complex mountainous environments. Overall, from 2000 to 2020, 66.75% of snow products successfully removed cloud coverage. This resulted in a decrease in the annual average cloud coverage rate from 52.46% to 34.41% when compared with the MOD10A1 snow product. Additionally, there was an increase in snow coverage rate from 21.52% to 33.84%. This improvement in cloud removal greatly enhanced the time resolution of snow cover data without compromising the accuracy of snow identification.

Assessing fluvial organic carbon flux and its response to short climate variability and damming on a large-scale tropical Asian river basin

Fluvial organic carbon (OC) transfer is an essential resource for downstream ecosystems. Multiple factors affect its transfer process, e.g., climate or anthropogenic activities. Quantifying OC fluxes with fine spatiotemporal resolution is challenging in anthropised catchments. This study aims to quantify daily OC dynamics and to assess the impacts of short climate variability and damming on OC spatiotemporal transfer processes in a large tropical Asian river basin (the Red River) for an extended period (2003-2013) by combining empirical equations with modelling outputs. Firstly, empirical equations for calculating dissolved (DOC) and particulate OC (POC) concentrations were calibrated based on in-situ sampling data. Then, simulated daily discharge (Q) and suspended sediment concentrations were used to quantify daily OC fluxes. Results show that the parameters of the DOC and POC equations well represent the subbasins characteristics, underlining the effects of soil OC content, mean annual Q and Chlorophyll a. DOC and POC exports reached 222 and 406 kt yr−1 at the basin outlet, accounting for 0.38 % of the total OC (TOC) exported by Asian rivers to the ocean. However, the specific yields of DOC (1.62 t km−2 yr−1) and POC (2.96 t km−2 yr−1) of the Red River basin were ~ 1.5 times those of other Asian basins. By comparing a reference scenario (without dams) to current conditions, we estimated 12 % and 88 % decreases in DOC and POC fluxes between 2008–2013 and 2003–2007, mainly due to damming. This study shows that climate variability may not impact OC dynamics in rivers as it explained <2 % of the variations. However, dam management, especially recent ones operating since 2008, deeply influences OC variations as the POC/TOC ratio decreased from 86 % to 47 %. Damming significantly decreased POC exports due to sediment retention, altering the equilibrium of OC cycling downstream, which may impact the food chain.

A new daily gridded precipitation dataset for the Chinese mainland based on gauge observations

Abstract. High-quality, freely accessible, long-term precipitation estimates with fine spatiotemporal resolution play essential roles in hydrologic, climatic, and numerical modeling applications. However, the existing daily gridded precipitation datasets over China are either constructed with insufficient gauge observations or neglect topographic effects and boundary effects on interpolation. Using daily observations from 2839 gauges located across China and nearby regions from 1961 to the present, this study compared eight different interpolation schemes that adjusted the climatology based on a monthly precipitation constraint and topographic characteristic correction, using an algorithm that combined the daily climatology field with a precipitation ratio field. Results from these eight interpolation schemes were validated using 45 992 high-density daily gauge observations from 2015 to 2019 across China. Of these eight schemes, the one with the best performance merges the Parameter-elevation Regression on Independent Slopes Model (PRISM) in the daily climatology field and interpolates station observations into the ratio field using an inverse-distance weighting method. This scheme had median values of 0.78 for the correlation coefficient, 8.8 mm d−1 for the root-mean-square deviation, and 0.69 for the Kling–Gupta efficiency for comparisons between the 45 992 high-density gauge observations and the best interpolation scheme for the 0.1∘ latitude × longitude grid cells from 2015 to 2019. This scheme had the best overall performance, as it fully considers topographic effects in the daily climatology field and it balances local data fidelity and global fitting smoothness in the interpolation of the precipitation ratio field. Therefore, this scheme was used to construct a new long-term, gauge-based gridded precipitation dataset for the Chinese mainland (called CHM_PRE, as a member of the China Hydro-Meteorology dataset) with spatial resolutions of 0.5, 0.25, and 0.1∘ from 1961 to the present. This precipitation dataset is expected to facilitate the advancement of drought monitoring, flood forecasting, and hydrological modeling. Free access to the dataset can be found at https://doi.org/10.6084/m9.figshare.21432123.v4 (Han and Miao, 2022).

Ground‐Validation and Error Attribution of Near‐Surface Air Temperature From AIRS in North America

AbstractAccurate estimation of near‐surface air temperature (TsurfAir) at fine spatiotemporal resolution is crucial for environmental applications, data assimilation into land surface models, and climate change studies. The Atmospheric Infrared Sounder (AIRS) on board the Aqua satellite measures TsurfAir twice a day at the global scale. In this study, the AIRS TsurfAir is validated using ground‐based weather stations in North America. Owing to the high density of weather stations, spatial heterogeneity (&lt;1°) of TsurfAir is modeled using semi‐variograms and further used to calculate the validation reference via ordinary kriging at AIRS retrieval footprints. This analysis intends to reveal error characteristics in cold and hot seasons by selecting six months of data in January and July of three years, along with a case study on two extreme events. Furthermore, MODIS‐enabled land surface temperature and land cover are used to attribute the observed error in TsurfAir. Results show an overall warm bias of 0.11 K from AIRS TsurfAir and conditional bias over‐ and underestimating cold and hot temperatures, respectively. Spatial heterogeneity of TsurfAir is found to influence the variance of AIRS TsurfAir error. As a possible source of TsurfAir error, land surface temperatures (Tskin) from AIRS and MODIS are compared. Close match between the two Tskin products indicates a lesser influence of the Tskin difference on TsurfAir error. Overall, the ground validation of AIRS TsurfAir along with the error attribution can facilitate post‐processing of AIRS products and uncertainty analysis for the wide range of applications of AIRS TsurfAir.

Periodicity and Variability in Daily Activity Satisfaction: Toward a Space-Time Modeling of Subjective Well-Being

Understanding how geographical environments influence peoples’ subjective experiences of daily activities is of great potential for improving subjective well-being (SWB), a subject that is presently limited by a lack of available data and proper statistical methods. Focusing on Beijing and using a unique data set that linked residents’ seven-day mobility trajectories at a fine spatiotemporal resolution to their complete activity participation and momentary well-being, this article investigates the temporal dynamics of and geographical contextual effects on SWB of daily activities. We developed a unified spatial multilevel stochastic process model to simultaneously capture periodicity, stochastic dynamics, and individual heterogeneity effects. Results show that momentary SWB has a twenty-four-hour periodicity and evolves stochastically around individual equilibrium states depending on key life-circumstance variables. Migrants and low-income residents tend to have lower equilibrium states than their counterparts. Real-time air pollution exposure significantly lowers daily activity satisfaction levels, and an inverted-U-shaped relationship exists between city vibrancy and satisfaction.

Inferring urban polycentricity from the variability in human mobility patterns

The polycentric city model has gained popularity in spatial planning policy, since it is believed to overcome some of the problems often present in monocentric metropolises, ranging from congestion to difficult accessibility to jobs and services. However, the concept ‘polycentric city’ has a fuzzy definition and as a result, the extent to which a city is polycentric cannot be easily determined. Here, we leverage the fine spatio-temporal resolution of smart travel card data to infer urban polycentricity by examining how a city departs from a well-defined monocentric model. In particular, we analyse the human movements that arise as a result of sophisticated forms of urban structure by introducing a novel probabilistic approach which captures the complexity of these human movements. We focus on London (UK) and Seoul (South Korea) as our two case studies, and we specifically find evidence that London displays a higher degree of monocentricity than Seoul, suggesting that Seoul is likely to be more polycentric than London.

Exploring the Future of Rainfall Extremes Over CONUS: The Effects of High Emission Climate Change Trajectories on the Intensity and Frequency of Rare Precipitation Events

AbstractThe impacts of climate change on extreme rainfall characteristics at fine spatiotemporal scales are governed by substantial uncertainty, primarily due to the systematic error components inherited from conventional numerical prediction systems, and/or the intrinsic assumptions of the selected modeling schemes. Here, we attempt to robustly evaluate the effects of future climate scenarios on intensity‐duration‐frequency (IDF) curves over the entire Contiguous United States, while accounting for the nonstationary nature of the rainfall process across adequately fine spatiotemporal resolutions. To do so, we apply a parametric approach to statistically downscaled climate model outputs that reflect the Representative Concentration Pathway 8.5, which are offered by the North American Coordinated Regional Downscaling Experiment. Compared to traditional IDF estimation techniques, the employed framework is based on multifractal (MF) scaling arguments and assumes that the statistical structure of rainfall at interannual scales can be approximated by sequential realizations of a stationary MF process with parameters that vary slowly across (not within) realizations. The obtained results show that return period estimates exhibit significant downward trends over most of the domain, which slowly dampen with time, as the effects of climate change are more pronounced at lower exceedance probability levels. Given the observed rate of changes in the frequency and intensity of extreme rainfall for the remainder of the century, we argue that future infrastructure design should be strategically tailored to account for a wide range of potential outcomes.

The evaluation and downscaling‐calibration of IMERG precipitation products at sub‐daily scales over a metropolitan region

AbstractThe Global Precipitation Measurement (GPM) Integrated Multi‐satellitE Retrievals for GPM products (i.e., IMERG) provide new‐generation satellite precipitation measurements. For urban contexts, however, the issues of its bias and insufficient resolutions still exist. This study aims to develop high‐precision and high‐resolution (e.g., 0.01°/1 h) data for a metropolitan region based on IMERG and gauge precipitations. The original IMERG product is evaluated using hourly in situ precipitations from 47 gauges. A spatial downscaling‐calibration (DC) technique is then developed to enhance the IMERG using the Normalized Difference Vegetation Index. The results show the limited capability of IMERG to capture sub‐daily precipitation and high‐intensity precipitation. The proposed DC method significantly improves IMERG performance, with correlation coefficient (CC) increasing from 0.07 to 0.75, and probability of detection improving from 0.34 to 0.90 at the hourly scale. In terms of spatial rainfall distribution, 86% of mean absolute error and 80% of RMSE are improved with CC increasing from 0.07 to 0.91 on average. Additionally, the calibrated downscaled product provides finer information in local areas, capturing three times more spatial variabilities of urban precipitation against the original IMERG input data. The results highlight the necessity of improving urban observations for flood risk management at fine spatiotemporal resolutions.

How do taxi usage patterns vary and why? A dynamic spatiotemporal analysis in Beijing

Existing studies lack attention to taxi usage dynamics, considering its trip proportion over other travel modes and its influencing factors at fine spatiotemporal resolutions. To fill these gaps, we propose a method for examining taxi usage in a grid of 1 km × 1 km cells per hour during a one-day cycle in Beijing. This method measures the differences between taxi trips from taxi trajectory data and mobile signaling data in the same week in January 2017. To explain the spatiotemporal variation in taxi usage, multiple linear models were used to investigate taxi usage dynamics with alternative transport modes, socioeconomic factors, and built environments. In summary, this study proposes to develop an indicator to measure taxi usage using multiple data sources. We confirm that taxi usage dynamics exist in both temporal and spatial dimensions. In addition, the effects of taxi usage factors vary over each hour in a one-day cycle. These findings are useful for urban planning and transport management, in which the dynamic interactions between taxi demand and distribution of facilities should be included.

Spatial and temporal non‐stationarity in long‐term population dynamics of over‐wintering birds of North America

Understanding population changes across long time scales and at fine spatiotemporal resolutions is important for confronting a broad suite of conservation challenges. However, this task is hampered by a lack of quality long-term census data for multiple species collected across large geographic regions. Here, we used century-long (1919-2018) data from the Audubon Christmas Bird Count (CBC) survey to assess population changes in over 300 avian species in North America and evaluate their temporal non-stationarity. To estimate population sizes across the entire century, we employed a Bayesian hierarchical model that accounts for species detection probabilities, variable sampling effort, and missing data. We evaluated population trends using generalized additive models (GAMs) and assessed temporal non-stationarity in the rate of population change by extracting the first derivatives from the fitted GAM functions. We then summarized the population dynamics across species, space, and time using a non-parametric clustering algorithm that categorized individual population trends into four distinct trend clusters. We found that species varied widely in their population trajectories, with over 90% of species showing a considerable degree of spatial and/or temporal non-stationarity, and many showing strong shifts in the direction and magnitude of population trends throughout the past century. Species were roughly equally distributed across the four clusters of population trajectories, although grassland, forest, and desert specialists more commonly showed declining trends. Interestingly, for many species, region-wide population trends often differed from those observed at individual sites, suggesting that conservation decisions need to be tailored to fine spatial scales. Together, our results highlight the importance of considering spatial and temporal non-stationarity when assessing long-term population changes. More generally, we demonstrate the promise of novel statistical techniques for improving the utility and extending the temporal scope of existing citizen science datasets.

Early Holocene plant macrofossils indicate cool refugia for subalpine plant taxa in Acadia National Park, Maine

Identifying refugia— specifically places where species can persist during periods of regionally unsuitable climate— is increasingly important for conservation practitioners and land managers charged with protecting biodiversity in a rapidly warming world. Currently, many researchers assist in this process by building models to predict areas of refugia using climate data projected into the future under different climate scenarios; however, the coarse spatial scale of future climate data can be orders of magnitude larger than the scale of refugia on the landscape. Conservation paleobiology is an emerging field that can contribute to the identification of climate refugia by looking at the macrofossil records contained in sediments to better understand the response of species to past climate change within a small area, and allows us to ground-truth hypotheses about specific areas functioning as climate refugia. Here, we present a conservation paleobiology case study to update vulnerability assessments for subalpine plant species in Acadia National Park and locate potential future refugia on the landscape. We analyzed plant macrofossils in a sediment core from Sargent Mountain Pond in Acadia National Park (Maine, United States) at a fine spatiotemporal resolution to test the hypothesis that the area served as a past climate refugium for the subalpine species it currently hosts. We found that, when compared to a pollen record from a forest hollow core collected on Mount Desert Island, the macrofossils reflect a more stable presence of subalpine taxa throughout the Holocene Climatic Optimum (8,000–5,000 BP) than was observed at lower elevations. Our results indicate the importance of a complementary approach that combines plant macrofossils and pollen in addition to modeling to identify refugia and better understand the vulnerability of species and communities to climate change.

A framework for estimating all-weather fine resolution soil moisture from the integration of physics-based and machine learning-based algorithms

Due to the effects of radio frequency interference and the limitations of algorithms under specific conditions, most of the currently available microwave-based soil moisture (SM) products are spatially discontinuous and have coarse spatial resolution, whereas optical observations also reveal various data gaps due to cloud contamination. Hence, the prediction of SM over invalid pixels and disaggregation from coarse to high scales are two main processes for obtaining SM at fine spatiotemporal resolution (e.g., daily/1-km). In the present study, two methods with respect to disaggregation-first or prediction-first were investigated from the synergetic use of the widely recognized European Space Agency-Climate Change Initiative (ESA-CCI) SM product and Moderate Resolution Imaging Spectroradiometer (MODIS) images over the Tibetan Plateau (TP) region. Specifically, the Disaggregation based on Physical And Theoretical scale Change (DisPATCh) algorithm and the generalized regression neural network (GRNN) were implemented in the disaggregation and prediction, respectively. In DisPATCh, spatially complete land surface temperature (LST), normalized difference vegetation index (NDVI) and digital elevation model (DEM) were provided as essential inputs to downscale the microwave-based ESA-CCI to a spatial resolution of 1 km, whereas MODIS-derived LST, NDVI, land surface albedo and DEM were considered in the GRNN prediction. Following the two methods, the daily/1-km SM dataset over a period of three years was finally estimated. Assessments with ground in-situ SM measurements over the TP region reveal an acceptable accuracy with unbiased root mean square errors of ∼ 0.06 m3/m3, indicating the potential to obtain operational daily/1-km spatially continuous SM products in future developments.