Limited Temporal Resolution Research Articles

Orbital CO2 reconstruction using boron isotopes during the late Pleistocene, an assessment of accuracy

Abstract. Boron isotopes in planktonic foraminifera are a widely used proxy to determine ancient surface seawater pH and by extension atmospheric CO2 concentration and climate forcing on geological timescales. Yet, to reconstruct absolute values for pH and CO2, we require a δ11Bforam-borate to pH calibration and independent determinations of ocean temperature, salinity, a second carbonate parameter, and the boron isotope composition of seawater. Although δ11B-derived records of atmospheric CO2 have been shown to perform well against ice-core-based CO2 reconstructions, these tests have been performed at only a few locations and with limited temporal resolution. Here we present two highly resolved CO2 records for the late Pleistocene from Ocean Drilling Program (ODP) Sites 999 and 871. Our δ11B-derived CO2 record shows a very good agreement with the ice core CO2 record with an average offset of 13±46 (2σ) and an RMSE of 26 ppm, with minor short-lived overestimations of CO2 (of up to ∼50 ppm) occurring during some glacial onsets. We explore potential drivers of this disagreement and conclude that partial dissolution of foraminifera has a minimal effect on the CO2 offset. We also observe that the general agreement between δ11B-derived and ice core CO2 is improved by optimising the δ11Bforam-borate calibration. Despite these minor issues, a strong linear relationship between relative change in climate forcing from CO2 (from ice core data) and pH change (from δ11B) exists over the late Pleistocene, confirming that pH change is a robust proxy of climate forcing over relatively short (<1 million year) intervals. Overall, these findings demonstrate that the boron isotope proxy is a reliable indicator of CO2 beyond the reach of the ice cores and can help improve determinations of climate sensitivity for ancient time intervals.

An AI approach to operationalise global daily PlanetScope satellite imagery for river water masking

Monitoring rivers is vital to manage the invaluable ecosystem services they provide, and also to mitigate the risks they pose to property and life through flooding and drought. Due to the vast extent and dynamic nature of river systems, Earth Observation (EO) is one of the best ways to measure river characteristics. As a first step, EO-based river monitoring often requires extraction of accurate pixel-level water masks, but satellite images traditionally used for this purpose suffer from limited spatial and/or temporal resolution. We address this problem by applying a novel Convolutional Neural Network (CNN)-based model to automate water mask extraction from daily 3 m resolution PlanetScope satellite imagery. Notably, this approach overcomes radiometric issues that frequently present limitations when working with CubeSat data. We test our classification model on 36 rivers across 12 global terrestrial biomes (as proxies for the environmental and physical characteristics that lead to the variability in catchments around the globe). Using a relatively shallow CNN classification model, our approach produced a median F1 accuracy score of 0.93, suggesting that a compact and efficient CNN-based model can work as well as, if not better than, the very deep neural networks conventionally used in similar studies, whilst requiring less training data and computational power. We further show that our model, specialised to the task at hand, performs better than a state-of-the-art Fully Convolutional Neural Network (FCN) that struggles with the highly variable image quality from PlanetScope. Although classifying rivers that were narrower than 60 m, anastomosed or highly urbanised was slightly less successful than our other test images, we showed that fine tuning could circumvent these limitations to some degree. Indeed, fine tuning carried out on the Ottawa River, Canada, by including just 5 additional site-specific training images significantly improved classification accuracy (F1 increased from 0.81 to 0.90, p < 0.01). Overall, our results show that CNN-based classification applied to PlanetScope imagery is a viable tool for producing accurate, temporally dynamic river water masks, opening up possibilities for river monitoring investigations where high temporal variability data is essential.

Utilization of Curvelet Transform in Reconstructing Cellular Images for Undersampled Optical-resolution Photoacoustic Microscopy.

We address the problem of limited temporal resolution in optical-resolution microscopy (OR-PAM) for cellular imaging by undersampling and reconstruction. A curvelet transform method in a compressed sensing framework (CS-CVT) was devised to specifically reconstruct the boundary and separability of cells object in an image. The performance of CS-CVT approach was justified by comparisons with the natural neighbor interpolation (NNI) followed by smoothing filters on various imaging objects. In addition, a full-raster scanned image was provided as reference. In terms of structure, CS-CVT produces cellular images with smoother boundary but less aberration. We found the strength of CS-CVT in recovering high frequency that is important in representing sharp edges which often missing in typical smoothing filter. In a noisy environment, CS-CVT was less affected by the noise compared to NNI with smoothing filter. Furthermore, CS-CVT could attenuate noise beyond the full raster scanned image. By considering the finest structure in the cellular image, CS-CVT was performing well with minimum range of undersampling around 5% to 15%. In practice, this undersampling were easily translates into 8- to 4-fold faster OR-PAM imaging. In summary, our approach improves temporal resolution of OR-PAM without significant trade-off in image quality.

Excitation lifetime extracted from electron–photon (EELS-CL) nanosecond-scale temporal coincidences

Electron–photon temporal correlations in electron energy loss spectroscopy (EELS) and cathodoluminescence (CL) spectroscopies have recently been used to measure the relative quantum efficiency of materials. This combined spectroscopy, named cathodoluminescence excitation (CLE) spectroscopy, allows for the identification of excitation and decay channels, which are hidden in average measurements. Here, we demonstrate that CLE can also be used to measure excitations' decay time. In addition, the decay time as a function of the excitation energy is measured, as the energy for each electron–photon pair is probed. We used two well-known insulating materials to characterize this technique, nanodiamonds with NV0 defects and hexagonal boron nitride (h-BN) with 4.1 eV defects. Both also exhibit marked transition radiations, whose extremely short decay times can be used to characterize the instrumental response function. It is found to be typically 2 ns, in agreement with the expected limit of the EELS detector temporal resolution. The measured lifetimes of NV0 centers in diamond nanoparticles (20–40 ns) and 4.1 eV defect in h-BN flakes (&amp;lt;2 ns) match those reported previously.

Estimating multi-scale irrigation amounts using multi-resolution soil moisture data: A data-driven approach using PrISM

Irrigated agriculture is the primary driver of freshwater use and is continuously expanding. Precise knowledge of irrigation amounts is critical for optimizing water management, especially in semi-arid regions where water is a limited resource. This study proposed to adapt the PrISM (Precipitation inferred from Soil Moisture) methodology to detect and estimate irrigation events from soil moisture remotely sensed data. PrISM was originally conceived to correct precipitation products, assimilating Soil Moisture (SM) observations into an antecedent precipitation index (API) formula, using a particle filter scheme. This novel application of PrISM uses initial precipitation and SM observations to detect instances of water excess in the soil (not caused by precipitation) and estimates the amount of irrigation, along with its uncertainty. This newly proposed approach does not require extensive calibration and is adaptable to different spatial and temporal scales. The objective of this study was to analyze the performance of PrISM for irrigation amount estimation and compare it with current state-of-the-art approaches. To develop and test this methodology, a synthetic study was conducted using SM observations with various noise levels to simulate uncertainties and different spatial and temporal resolutions. The results indicated that a high temporal resolution (less than 3 days) is crucial to avoid underestimating irrigation amounts due to missing events. However, including a constraint on the frequency of irrigation events, deduced from the system of irrigation used at the field level, could overcome the limitation of low temporal resolution and significantly reduce underestimation of irrigation amounts. Subsequently, the developed methodology was applied to actual satellite SM products at different spatial scales (1 km and 100 m) over the same area. Validation was performed using in situ data at the district level of Algerri-Balaguer in Catalunya, Spain, where in situ irrigation amounts were available for various years. The validation resulted in a total Pearson’s correlation coefficient (r) of 0.80 and a total root mean square error (rmse) of 7.19 mm∕week for the years from 2017 to 2021. Additional validation was conducted at the field level in the Segarra-Garrigues irrigation district using in situ data from a field where SM profiles and irrigation amounts were continuously monitored. This validation yielded a total bi-weekly r of 0.81 and a total rmse of −9.34 mm∕14-days for the years from 2017 to 2021. Overall, the results suggested that PrISM can effectively estimate irrigation from SM remote sensing data, and the methodology has the potential to be applied on a large scale without requiring extensive calibration or site-specific knowledge.

Estimating IDF curves under changing climate conditions for different climate regions

Abstract Although climate models can highlight potential shifts in intensity–duration–frequency (IDF) curves, their limited geographical and temporal resolutions limit their direct use in predicting sub-daily heavy precipitation. To use global or regional model outputs to predict urban short-term precipitation, approaches that give the requisite level of spatial and temporal downscaling are required, and these processes remain one of the difficulties that have demanded intensive effort in recent years. Although no novel methods are given in this work, there are few studies in the literature that investigate the impact of climate change on the analysis and design of infrastructure-related engineering structures. Therefore, the purpose of this research is to determine the potential changes in IDF curves because of climate change. The equidistance quantile matching method was used to turn future rainfall forecast data from global climate models (HadGEM2-ES, MPI-ESM-MR, and GFDL-ESM2M) corresponding to RCP4.5 and RCP8.5 scenarios into standard duration rainfall data, and new IDF curves were generated. These IDF curves corresponded very well with those generated from observed data (R2 ≈ 1). The HadGEM2-ES model predicts up to a 25% rise in rainfall intensity, whereas the other two models expect up to a 50% drop.

Assessment of the Value of Remotely Sensed Surface Water Extent Data for the Calibration of a Lumped Hydrological Model

AbstractFor many catchments, there is insufficient field data to calibrate the hydrological models that are needed to answer water resources management questions. One way to overcome this lack of data is to use remotely sensed data. In this study, we assess whether Landsat‐based surface water extent observations can inform the calibration of a lumped bucket‐type model for Brazilian catchments. We first performed synthetic experiments with daily, monthly, and limited monthly data (April–October), assuming a perfect monotonic relation between streamflow and stream width. The median relative performance was 0.35 for daily data and 0.17 for monthly data, where values above 0 imply an improvement in model performance compared to the lower benchmark. This indicates that the limited temporal resolution of remotely sensed data is not an impediment for model calibration. In a second step, we used real remotely sensed water extent data for calibration. For only 76 of the 671 sites the remotely sensed water extent was large and variable enough to be used for model calibration. For 30% of these sites, calibration with the actual remotely sensed water extent data led to a model fit that was better than the lower benchmark (i.e., relative performance &gt;0). Model performance increased with river width and variation therein. This indicates that the coarse spatial resolution of the freely‐available, long time series of water extent used in this study hampered model calibration. We, therefore, expect that newer higher‐resolution imagery will be helpful for model calibration for more sites, especially when time series length increases.

All-weather precipitable water vapor map reconstruction using data fusion and machine learning-based spatial downscaling

Precipitable water vapor (PWV) detection with high spatial resolution and high accuracy is of significant importance for contributing to extreme weather events monitoring and forecasting. Current PWV products, however, suffer from limitations of spatial and temporal discontinuities, low accuracy, and coarse spatial resolution. To overcome this problem, a data fusion and machine learning-based spatial downscaling solution is proposed. At first, spatially complete PWV maps are generated by integrating calibrated PWV of Moderate Resolution Imaging Spectroradiometer (MODIS) and the ERA5 PWV data from 2018 to 2022. Subsequently, three spatial downscaling models based on Gradient Boosting Decision Tree (GBDT), Multi-layer Perceptron Neural Network (MLPNN), and Random Forest (RF), respectively, are developed to produce high-quality, all-weather PWV considering the land-cover type. It has been verified that the high-quality all-weather PWV maps generated by the GBDT, MLPNN, and RF models exhibit strong agreement with Global Navigation Satellite System (GNSS) PWV estimates. The correlation coefficients are 0.95, 0.87, and 0.87, while the overall Bias is 0.29 mm, 0.67 mm, and 0.35 mm, and the root mean square errors (RMSE) are 1.74 mm, 2.98 mm, and 3.06 mm, respectively. These results significantly enhance the accuracy of MODIS PWV products (R2 = 0.73, RMSE = 5.64 mm, Bias = 3.05 mm). Notably, the GBDT model outperforms the other models in terms of performance. Compared to MODIS PWV, the new PWV map with a data fusion and machine learning-based spatial downscaling approach effectively utilizes the advantage of satellite-based and reanalyzed PWV products, providing continuous, detailed, and reasonable variation in time and space. Moreover, it is less influenced by seasonal changes. The new PWV map has a promising application for regional hydrology and meteorology.

Quantifying early age hydration and micro-structure evolution of fast setting binder using 1H NMR

The first hours' hydration of fast-setting binder is difficult to monitor due to the limited temporal resolution of common micro-structure-chemistry probes and the complicated product compositions. This study explored using T2 relaxation time distribution data from in-situ 1H nuclear magnetic resonance (NMR) measurement to quantify the early-hydration kinetics of two OPC/CAC/calcium sulfate ternary binders. The calculated phased composition and porosity evolution were compared with evidence from in-situ XRD, SEM, TGA, temperature and tensile splitting strength evolution. We demonstrate that the deconvoluted T2 signal of water can provide a reliable estimation of microstructural information, e.g., the ettringite product and the porosity. There exists a linear relationship between the averaged T2 signal and the porosity of the paste. In addition, the averaged T2 signal provides a good index of strength, which applies equally to all studied samples despite their different in the cement type, w/c and presence of fine aggregates.

Deep-learning image enhancement and fibre segmentation from time-resolved computed tomography of fibre-reinforced composites

Monitoring the microstructure and damage development of fibre-reinforced composites during loading is crucial to understanding their mechanical properties. Time-resolved X-ray computed tomography enables such an in-situ, non-destructive study. However, the photon flux and fibre-matrix contrast limit its achievable spatial and temporal resolution. In this paper, we push the limits of temporal and spatial resolution for the microstructural analysis of unidirectional continuous carbon fibre-reinforced epoxy composites by establishing a new pipeline based on CycleGAN for unsupervised super-resolution and denoising and U-Net-id for individual fibre segmentation. After illustrating the benefits of a 3D CycleGAN over a 2D one, we show that data enhanced by this pipeline can yield similar segmentation quality to that of a slow-acquisition, high-quality scan that took up to 200 times longer to acquire. This pipeline, therefore, enables more robust data extraction from fast time-resolved X-ray tomography, removing a critical stumbling block for this technique.

Spatiotemporal variation of cyanobacterial harmful algal blooms in China based on literature and media information

ABSTRACT Cyanobacterial harmful algal blooms (CyanoHABs) in inland waters are now among the most pressing environmental issues worldwide, especially in China. Satellite remote sensing has limitations in monitoring CyanoHABs in small water bodies due to spatial and temporal resolution limitations. While literature and news media have the potential to supplement satellite remote sensing in monitoring CyanoHABs, they have currently not received sufficient attention. In this study, we combined information on the distributions of CyanoHABs from literature and media for the first time to comprehensively assess the spatiotemporal variation in CyanoHABs in China. We collected, cleaned, validated, and organized data from literature and media on CyanoHABs in China, resulting in the establishment of a comprehensive database on CyanoHABs in China's inland waters (ChinaCyanoDB) covering 198 water bodies, 525 records for 1950–2021. The majority of water bodies with CyanoHABs (CyanoWaters) are located in the eastern China, mainly concentrated in the middle and lower Yangtze region, with a clear upward trend in their number over the last four decades. The ChinaCyanoDB and analytical results can provide valuable data support for monitoring and managing CyanoHABs in China while the database construction method may also be applied to other countries and regions.

Intra-frame motion deterioration effects and deep-learning-based compensation in MR-guided radiotherapy.

Current commercially available hybrid magnetic resonance linear accelerators (MR-Linac) use 2D+t cine MR imaging to provide intra-fractional motion monitoring. However, given the limited temporal resolution of cine MR imaging, target intra-frame motion deterioration effects, resulting in effective time latency and motion artifacts in the image domain, can be appreciable, especially in the case of fast breathing. The aim of this work is to investigate intra-frame motion deterioration effects in MR-guided radiotherapy (MRgRT) by simulating the motion-corrupted image acquisition, and to explore the feasibility of deep-learning-based compensation approaches, relying on the intra-frame motion information which is spatially and temporally encoded in the raw data (k-space). An intra-frame motion model was defined to simulate motion-corrupted MR images, with 4D anthropomorphic digital phantoms being exploited to provide ground truth 2D+t cine MR sequences. A total number of 10 digital phantoms were generated for lung cancer patients, with randomly selected eight patients for training or validation and the remaining two for testing. The simulation code served as the data generator, and a dedicated motion pattern perturbation scheme was proposed to build the intra-frame motion database, where three degrees of freedom were designed to guarantee the diversity of intra-frame motion trajectories, enabling a thorough exploration in the domain of the potential anatomical structure positions. U-Nets with three types of loss functions: L1 or L2 loss defined in image or Fourier domain, referred to as NNImgLoss-L1 , NNFloss-L1 and NNL2-Loss were trained to extract information from the motion-corrupted image and used to estimate the ground truth final-position image, corresponding to the end of the acquisition. Images before and after compensation were evaluated in terms of (i) image mean-squared error (MSE) and mean absolute error (MAE), and (ii) accuracy of gross tumor volume (GTV) contouring, based on optical-flow image registration. Image degradation caused by intra-frame motion was observed: for a linearly and fully acquired Cartesian readout k-space trajectory, intra-frame motion resulted in an imaging latency of approximately 50% of the acquisition time; in comparison, the motion artifacts exhibited only a negligible contribution to the overall geometric errors. All three compensation models led to a decrease in image MSE/MAE and GTV position offset compared to the motion-corrupted image. In the investigated testing dataset for GTV contouring, the average dice similarity coefficients (DSC) improved from 88% to 96%, and the 95th percentile Hausdorff distance (HD95 ) dropped from 4.8mm to 2.1mm. Different models showed slight performance variations across different intra-frame motion amplitude categories: NNImgLoss-L1 excelled for small/medium amplitudes, whereas NNFloss-L1 demonstrated higher DSC median values at larger amplitudes. The saliency maps of the motion-corrupted image highlighted the major contribution of the later acquired k-space data, as well as the edges of the moving anatomical structures at their final positions, during the model inference stage. Our results demonstrate the deep-learning-based approaches have the potential to compensate for intra-frame motion by utilizing the later acquired data to drive the convergence of the earlier acquired k-space components.

Functional magnetic particle imaging (fMPI) of cerebrovascular changes in the rat brain during hypercapnia

Objective. Non-invasive functional brain imaging modalities are limited in number, each with its own complex trade-offs between sensitivity, spatial and temporal resolution, and the directness with which the measured signals reflect neuronal activation. Magnetic particle imaging (MPI) directly maps the cerebral blood volume (CBV), and its high sensitivity derives from the nonlinear magnetization of the superparamagnetic iron oxide nanoparticle (SPION) tracer confined to the blood pool. Our work evaluates functional MPI (fMPI) as a new hemodynamic functional imaging modality by mapping the CBV response in a rodent model where CBV is modulated by hypercapnic breathing manipulation. Approach. The rodent fMPI time-series data were acquired with a mechanically rotating field-free line MPI scanner capable of 5 s temporal resolution and 3 mm spatial resolution. The rat’s CBV was modulated for 30 min with alternating 5 min hyper-/hypocapnic states, and processed using conventional fMRI tools. We compare our results to fMRI responses undergoing similar hypercapnia protocols found in the literature, and reinforce this comparison in a study of one rat with 9.4T BOLD fMRI using the identical protocol. Main results. The initial image in the time-series showed mean resting brain voxel SNR values, averaged across rats, of 99.9 following the first 10 mg kg−1 SPION injection and 134 following the second. The time-series fit a conventional General Linear Model with a 15%–40% CBV change and a peak pixel CNR between 12 and 29, 2–6× higher than found in fMRI. Significance. This work introduces a functional modality with high sensitivity, although currently limited spatial and temporal resolution. With future clinical-scale development, a large increase in sensitivity could supplement other modalities and help transition functional brain imaging from a neuroscience tool focusing on population averages to a clinically relevant modality capable of detecting differences in individual patients.

Non-Markov models of single-molecule dynamics from information-theoretical analysis of trajectories.

Whether single-molecule trajectories, observed experimentally or in molecular simulations, can be described using simple models such as biased diffusion is a subject of considerable debate. Memory effects and anomalous diffusion have been reported in a number of studies, but directly inferring such effects from trajectories, especially given limited temporal and/or spatial resolution, has been a challenge. Recently, we proposed that this can be achieved with information-theoretical analysis of trajectories, which is based on the general observation that non-Markov effects make trajectories more predictable and, thus, more "compressible" by lossless compression algorithms. Toy models where discrete molecular states evolve in time were shown to be amenable to such analysis, but its application to continuous trajectories presents a challenge: the trajectories need to be digitized first, and digitization itself introduces non-Markov effects that depend on the specifics of how trajectories are sampled. Here we develop a milestoning-based method for information-theoretical analysis of continuous trajectories and show its utility in application to Markov and non-Markov models and to trajectories obtained from molecular simulations.

High visual resolution interpretation: The case for virtual seismic reality

The twin fields of virtual and augmented reality have revolutionized the gaming and entertainment industries; however, they have had almost no impact on the field of scientific visualization. This is especially true in oil and gas exploration where we continue to visualize seismic data using low visual resolution displays developed in the 1960s and 1970s. Variable density and grayscale displays were a revolution in themselves, allowing us to transition from strictly manual interpretation on paper sections to increasingly automatic interpretations on workstations. This transition was instrumental in allowing us to find the oil necessary to meet the demands of emerging economies. These displays have brought us this far, but they cannot take us into the future. Today, we are exploring for targets whose seismic expression is close to the limits of spatial and temporal resolution and may be below the visual resolution of conventional seismic displays. If we are to meet the current demands of developed economies and the increasing demands of emerging economies, we must replace these, now technologically archaic, low visual resolution displays with high visual resolution displays. For that, we need virtual reality. At its inception, virtual reality was largely ignored by the exploration industry. Today, it has evolved to the point that it could revolutionize scientific visualization, and seismic visualization in particular, as much as it revolutionized gaming and entertainment. I introduce the subject of high visual resolution interpretation and present examples of seismic data in virtual seismic reality.

Flow Heterogeneity and Factors Contributing to the Variability in Retinal Capillary Blood Flow.

Capillary flow plays an important role in the nourishment and maintenance of healthy neural tissue and can be observed directly and non-invasively in the living human retina. Despite their importance, patterns of normal capillary flow are not well understood due to limitations in spatial and temporal resolution of imaging data. Capillary flow characteristics were studied in the retina of three healthy young individuals using a high-resolution adaptive optics ophthalmoscope. Imaging with frame rates of 200 to 300 frames per second was sufficient to capture details of the single-file flow of red blood cells in capillaries over the course of about 3seconds. Erythrocyte velocities were measured from 72 neighboring vessels of the parafoveal capillary network for each subject. We observed strong variability among vessels within a given subject, and even within a given imaged field, across a range of capillary flow parameters including maximum and minimum velocities, pulsatility, abruptness of the systolic peak, and phase of the cardiac cycle. The observed variability was not well explained by "local" factors such as the vessel diameter, tortuosity, length, linear cell density, or hematocrit of the vessel. Within a vessel, a moderate relation between the velocities and hematocrit was noted, suggesting a redistribution of plasma between cells with changes in flow. These observations advance our fundamental understanding of normal capillary physiology and raise questions regarding the potential role of network-level effects in explaining the observed flow heterogeneity.

Functional neuroimaging options for tinnitus

The pathophysiology behind tinnitus is still not well understood. Different imaging methods help in the understanding of the complex relationships that lead to the perception of tinnitus. Herein, different functional imaging methods that can be used in the study of tinnitus are presented. Considering the recent literature on the subject, the relevant imaging methods used in tinnitus research are discussed. Functional imaging can reveal correlates of tinnitus. Due to the still limited temporal and spatial resolution of current imaging modalities, aconclusive explanation of tinnitus remains elusive. With increasing use of functional imaging, additional important insights into the explanation of tinnitus will be gained in the future.

Acoustic recording complements camera traps for monitoring sensitive penguin populations

In the face of global biodiversity loss that threatens social–ecological sustainability, we must improve our knowledge of species' behaviours, life histories and populations. Enhanced understanding is especially critical for taxa like seabirds that live in remote habitats where monitoring is logistically challenging, and technology‐based approaches could lead to major advances. Considering conservation threats and breeding behaviours, penguins represent a promising taxon for further exploration of monitoring methods. Only five of 18 penguin species are currently considered species of Least Concern by the IUCN, and the sensitivity of penguins to ecological disturbances makes them important indicators of ecosystem dynamics. Penguins regularly congregate in dense breeding colonies and are visually and aurally conspicuous animals, making them relatively easy to monitor in some respects. In situ observations and visual remote sensing monitoring methods (i.e. camera traps, uncrewed aerial vehicles and satellite imagery) currently provide highly valuable information on penguin behaviour and population dynamics, but these methods have a number of shortcomings. Several rely on human presence in remote areas, some require clean lines of sight with no visual obstructions, and some offer limited spatial and temporal resolution. In this study we explore the use of passive acoustic monitoring (PAM) as a complementary remote sensing method to monitor penguin behaviour and populations at high spatial and temporal resolution without visual constraints or the need for continuous human presence. We conducted observations of vocalization rates and placed automated acoustic recorders in colonies of Magellanic Penguins Spheniscus magellanicus and Southern Rockhopper Penguins Eudyptes chrysocome in conjunction with camera traps. We found positive relationships between acoustic activity and counts of Magellanic Penguins in camera trap photos. We also identified clear diel patterns of acoustic activity that differed between breeding stages, and we found positive correlations between acoustic activity and estimates of colony density for various times of day and radii around recorders. While much work remains to improve this method and refine interpretation, PAM holds great promise as a complementary tool for monitoring the relative abundance and behavioural dynamics of penguins and other colonial animals, particularly those that burrow or nest in dense vegetation that impairs visual monitoring techniques.

Integrating ICESat-2 altimetry and machine learning to estimate the seasonal water level and storage variations of national-scale lakes in China

Lakes comprise the largest element of terrestrial surface liquid freshwater bodies, playing an indispensable part in the Earth's water cycle and alleviating floods and droughts. The seasonal variation of lake water level (LWLsv) and storage (LWSsv) reflects the periodic hydrologic fluctuations and related driving forces of the water balances at the basin scale. China, a vastly diverse country that descends from the “Roof of the World” to monsoonal coast zones, hosts a wide distribution of lakes with diverse hydro-climatological and topographic features. Most previous studies focused on monitoring long-term changes of lakes in China, while the seasonality (mostly regarding the lake area) of lake hydrologic dynamics was only investigated for typical lakes or local basins/zones due to the limited spatial coverage and temporal resolution of various observation data. Benefiting from the finer footprints and increased beams of ICESat-2 laser altimeter, we examined the seasonal variations of LWLsv of Chinese lakes (3473 lakes historically larger than 1 km2 during the 1980s–2010s) on a national scale from 2019 to 2021. Then, the machine learning algorithm termed the extreme gradient boosting tree was employed to model the LWLsv of the lakes that were not monitored by ICESat-2. We further quantized the national-scale LWSsv by combining the LWLsv estimates and lake area data. Results show that the mean LWLsv of the 1255 lakes observed by ICESat-2 during 2019–2021 is 0.44 ± 0.07 m. Among them, 1167 lakes have the LWLsv <1 m, and 12 lakes have the LWLsv exceeding 3 m. The accuracy evaluation indicates that the XGBoost model performs well in predicting LWLsv results for unobserved lakes, with the coefficient of determination of 0.76, the mean absolute error of 0.14 m, and the root mean square error of 0.03 m. Overall, the predicted LWLsv of unobserved lakes has a similar spatial pattern to that of the observed lakes. The LWSsv is estimated at 77.29 ± 6.87 Gt in total, but exhibits obvious spatial heterogeneity in China. The Middle and Lower Reaches of Yangtze River Basin and the endorheic Qiangtang Plateau Basin rank the first two contributors of the net LWSsv, which were respectively attributed to the most significant LWLsv changes and the largest lake group area. This national-scale quantification of the LWLsv and LWSsv helps advance our scientific understanding of the seasonal hydrologic behaviors for Chinese lakes in regulating the water cycle and providing a valuable reference for regional water resource management.

Controlling factor analysis of oceanic surface pCO2 in the South China Sea using a three-dimensional high-resolution biogeochemical model

The oceanic surface pressure of CO2 (pCO2) is an essential parameter for understanding the global and regional carbon cycle and the oceanic carbon uptake capacity. We constructed a three-dimensional physical-biogeochemical model with a high resolution of 1/30° for the South China Sea (SCS) to compensate for the limited temporal coverage and limited spatial resolution of the observations and numerical models. The model simulated oceanic surface pCO2 from 1992 to 2021, and the empirical orthogonal function analysis (EOF) of the model results is conducted for a better understanding of the seasonal and interannual variations of oceanic surface pCO2 in this region. The model results showed that the SCS serves as an atmospheric CO2 source from March to October and a sink from November to February, with a domain-averaged climatological oceanic surface pCO2 value that varies between 357 and 408 μatm, and the temporal variation was positively correlated with the variation of sea surface temperature (SST). The majority of the SCS showed a long-term increasing trend for oceanic surface pCO2 with a value of (1.19±0.60) μatm/a, which is in response to the continuously rising atmospheric CO2 concentration. The first EOF mode is positively correlated with the Niño 3 index with a correlation coefficient of 0.51 when the Niño 3 leads 5 months, and the second EOF mode is correlated with the PDO index when the PDO leads 7 months, which suggests an influence of climate variability on the carbonate system. Moreover, it was found that the long-term trend rate of oceanic surface pCO2 was mainly controlled by total CO2 (TCO2) through the decomposition of influence factors, and SST variation took a dominant role in seasonal variations of pCO2. With rapid global warming and continuous release of CO2, the carbonate system in the SCS may change leading to calcite and aragonite saturation.