Study Region Research Articles

Mothers’ health-seeking practices and associated factors towards neonatal danger signs in Ethiopia: a systematic review and meta-analysis

BackgroundA decrease in obtaining quality healthcare is a major cause of maternal and newborn deaths in low-income and middle-income countries. Ethiopia has one of the highest neonatal mortality rates. Increasing...

Wudalianchi volcanism and mantle dynamics in Northeast China: New insight from Pn and Sn tomography

We determine new tomographic models of Pn anisotropic velocity and Sn isotropic velocity in and around the Wudalianchi volcanic area by inverting high-quality Pn and Sn arrival times manually picked from waveforms recorded at the newly deployed high-dense WAVESArray portable seismic stations. Our high-resolution Pn and Sn velocity models reveal strong lateral heterogeneities in the uppermost mantle under the study region. The average Pn and Sn velocities in the uppermost mantle are 8.2 and 4.5 km/s, respectively. Both Pn and Sn velocity models exhibit obvious low-velocity (low-V) anomalies under the Wudalianchi and Keluo volcanoes, whereas under the Songliao basin distinct high-velocity (high-V) zones are revealed. In particular, our Pn model reveals two separate low-V anomalies under the Nuomihe and Halaha volcanic groups, suggesting that they have different deep origins. A large-scale L-shaped low-V zone exists under the Keluo, Wudalianchi, Erkeshan, and Xunke volcanoes, characterized by Pn-wave fast propagation directions (FPDs) parallel with the low-V zone, suggesting that these volcanoes may have the same deep origin. Furthermore, southeast-opened U-shaped Pn FPDs exist around the Wudalianchi volcano, whereas NE-SW FPDs appear under the Great Xing'an range, which are generally consistent with SKS splitting measurements. This feature may reflect lithosphere-asthenosphere coupling beneath the Wudalianchi volcano associated with horizontal flows in the big mantle wedge and compressional tectonics under the Great Xing'an range. These results shed new lights on the Wudalianchi volcanism and mantle dynamics beneath Northeast China.

Influence of local meteorology and gaseous pollutant emissions on atmospheric nanoparticle concentrations in a pedestrian way in urban region

The roadside environment is one of the major sources of nanoparticle emission in the urban regions. The complex mixture of different pollutants makes it hard to understand the dynamics and behavior of nanoparticles. The study aims to analyze the dynamics of atmospheric nanoparticles ranging from 10 to 1000 nm along with local meteorological conditions and gaseous pollutants in a pedestrian way at a busy street in Delhi, a megalopolis in India, during all five major seasons of the study area. Nucleation mode particles were higher during the spring season, whereas the contribution of Aitken mode particles dominated the total particle number concentrations in the rest of the seasons. Due to the influence of vehicular sources, rush hour concentrations were higher than during non-rush hours. Thus, the diurnal pattern in particle concentration strongly coincided with emissions associated with the vehicular flow. During the winter season, the average total particle number concentration was observed to be maximum (4.1 × 104 cm-3) with a higher surface area of particles of 3.5 × 10-3 mm2m-3. Compared to the monsoon season, the concentration of NOX was 5 times higher in winter. The boundary layer height in the study region ranged from 600 to 2400 m during different seasons, and the maximum ventilation coefficient was observed to be >3000 m2s-1 during summer. Precipitation reduced the concentration of particles by half, from 2.2 x 104 to 1.1 x 104 cm-3, due to wet scavenging. The study revealed that the concentrations of particles depend not only on primary emissions but also are influenced by local meteorology and other co-emitted pollutants. Understanding the dynamics of atmospheric nanoparticles in urban roadside environments as outlined in this study is crucial to devise necessary mitigation measures for people residing near the road in order to reduce health impact and improve air quality.

Aerosol retrievals derived from a low-cost Calitoo sun-photometer taken on board a research vessel

This study presents a comprehensive 5-year period assessment of aerosol optical depth (AOD) and Å ngströn Exponent (AE) data from a hand-held Calitoo sun photometer on board the Ángeles Alvariño research vessel. Observations spanned March 2018 to September 2023, focusing on key maritime regions such as the Canary Islands, coasts of North Africa, the Mediterranean, Portugal, the Cantabrian, and the Bay of Biscay. The Calitoo device measures solar irradiance at three wavelengths (465, 540, and 619 nm). Uncertainty analysis for Calitoo AOD retrievals was performed using the Monte Carlo method, yielding an expanded uncertainty (UAOD) ranging between 0.008 and 0.050 with a mean and standard deviation of 0.032 ± 0.008 for the three wavelengths. Our results also highlight the remarkable calibration stability of the Calitoo (< 2.6%) over this 5-year period. Calitoo AOD values were assessed using reference AOD data from Santa Cruz de Tenerife (the Canary Islands), El Arenosillo (Huelva), and Palma de Mallorca (the Balearic Islands) AERONET (Aerosol Robotic Network) stations. The comparison revealed a good agreement with correlation coefficients ranging from 0.727 to 0.917 and mean bias ranging from -0.030 to -0.001. Additionally, the Calitoo AOD data were compared with MODIS (Moderate Resolution Imaging Spectroradiometer) and CAMS-ECMWF (Copernicus Atmosphere Monitoring Service-European Centre for Medium-Range Weather Forecasts) aerosol products obtaining that Calitoo AOD values were generally lower, showing negative mean bias of -0.063 and -0.024, respectively.The aerosol characterizations using AE vs. AOD plots in the three maritime study regions using 5-years of non-routine Calitoo data are similar to the corresponding aerosol characterizations performed with simultaneous AERONET-Cimel data.These findings underscore Calitoo’s reliability for aerosol studies in regions where AERONET instruments or other aerosol networks are unavailable. Likewise, given the low cost of Calitoo photometers, they could be deployed onboard a large number of merchant and passenger ships or in other remote or under-monitored areas, providing near real-time AOD/AE data to enhance our understanding of aerosols processes or for model or satellite assimilation/validation.

Modeling NPP and NDVI time series in different bioclimatic regions of Iran.

Vegetation is one of the important components of ecosystems that usually changes seasonally. An accurate parameterization of vegetation cover dynamics by developing time series models can strengthen our understanding of vegetation change. This research aims to investigate and model the temporal changes of net primary production (NPP) and normalized difference vegetation index (NDVI) across bioclimatic regions of Iran, including the Khazari, Baluchi, semi-desert, steppe, semi-steppe, and arid forests. We used Moderate Resolution Imaging Spectroradiometer (MODIS) sensor products for NPP and NDVI time series (MOD17A2 and MOD13Q1, respectively). The SARIMA (Seasonal Autoregressive Integrated Moving Average) time series model is developed for NPP and NDVI time series. The investigation of autocorrelation functions (ACF) showed a strong seasonality in NPP and NDVI at the 12-month lag time. Comparing the lag times from 1 to 24 month for different regions shows that the NPP variable has a stronger seasonality. The evaluation of error criteria which showed NPP time series models based on RMSE, R2, MRE, and CE criteria was better, while based on the ME criteria, the models perform better for NDVI time series (for example, in Khazari region for NPP and NDVI time series, respectively, ME = 3.67, 0.05, RMSE = 0.12, 0.18, R2 = 0.87, 0.63, MRE = 0.02, 0.12, and CE = 0.84, 0.12). The selected models provided a short-term forecasting of the NPP and NDVI index for study regions at 24-month time, which is useful for the planning and management to reduce vegetation degradation and preserve ecosystem and biodiversity.

Analysis of lake level fluctuations in the Early Cretaceous Songliao Basin supports aquifer eustacy

Clear geochronology and precise interpretation of sequence stratigraphy enhance our understanding of continental lake-level evolution. The Songliao Basin features well-preserved Cretaceous continental strata. Nevertheless, the correlation between lake-level fluctuations and global sea-level changes remains ambiguous. High-resolution gamma ray logging data were used to analyze the cyclical stratigraphy of the lower member of the Early Cretaceous Shahezi Formation in the Songliao Basin. X-ray fluorescence experiments characterized the sedimentary environment of this formation. Lake-level variations in the lower part of the Early Cretaceous Shahezi Formation were reconstructed using sedimentary noise modeling based on finely tuned gamma ray logging data. Time series analysis using the tuned gamma ray data established an astronomical timescale of approximately 2.4 Myr within the lower section of the early Cretaceous Shahezi Formation in the Songliao Basin. A volcanic ash layer dating (118.20 ± 1.5) Ma from the base of the Shahezi Formation served as an anchor point, providing an absolute astronomical timescale ranging from 115.80 to 118.20 Ma for the study region. The sedimentary model indicates that variations in paleowater depth within the lower section of the Shahezi Formation closely match fluctuations observed in the Fe/Mn index, which reflects paleowater depth changes. This introduces a novel approach to assess changes in continental lake levels. The sedimentary noise model revealed a notable obliquity cycle of about 1.2 million years, strongly associated with fluctuations in lake levels. This indicates that prolonged obliquity periods affect lake level variations. Intriguingly, when lake levels rise, global sea levels concurrently decline, highlighting an inverse relationship between these phenomena. This observation offers insights into how long-term obliquity-driven climate changes regulate sea and lake levels.

Assessment of natural radioactivity in the Higher and Tethys Himalayan Rocks along Manali-Leh Highway, India.

The Higher and Tethys Himalayan region of NW-Himalaya is less explored from the natural radioactivity mapping assessment, though geologically and tectonically, this region is still active. The concentration of primordial radionuclides (226Ra, 232Th, and 40K) in rock samples of the Manali-Leh Highway region of the Himalayas is determined in the present study using the HPGe detector. The radiological hazard parameters are also estimated in terms of radium equivalent activity (Raeq), annual effective dose (Ein and Eout), hazard indices (Hin, Hex, Hα, and Hγ), and gamma absorbed dose rate. SEM-EDS analysis was used to understand the mineralogical composition of the rocks. The activity concentration of 226Ra, 232Th, and 40K radionuclides varies from 1.4 ± 0.9 to 25.3 ± 1.2, 0.7 ± 0.5 to 59.6 ± 1.6, and BDL (below detection limit) to 830.3 ± 45.7Bqkg-1, with an average value of 13.0 ± 1.0, 21.7 ± 1.0, and 243.7 ± 25.2Bqkg-1, respectively. The average of Raeq in the study region lies within the safe limit of 370Bqkg-1. The hazard indices have values < 1, indicating no radiological hazards to the population from the rocks. The annual effective dose also has values less than the global average value. This study revealed that the rocks of the Higher Himalayan region have a higher concentration of natural radioactivity, while the Tethys Himalayan rocks have lower concentrations of 226Ra, 232Th, and 40K radionuclides. The origin of rocks from different lithologies may be the reason for the natural radioactivity variation. The average concentrations of primordial radionuclides in the region are within the world average indicating that the rocks of the region are safe to be used for different purposes.

Microbial distribution in Mudbank regions off Alappuzha, South-West coast of India.

The coastal waters of Kerala, in the South Eastern Arabian Sea (SEAS), are unique during the Southwest monsoon season due to the concurrent occurrence of two physical processes, the upwelling and Mudbanks. However, little is known about the viral ecology and activity in a system where upwelling and Mudbanks coexist, though it is generally recognized that microbial assemblages play a vital role in the food web dynamics of marine systems, particularly in upwelling. Water samples were taken from monsoon and pre-monsoon period from three locations, (M1, M2, and M3) off Alappuzha, on the southwest coast of India to examine the viral activity and distribution. The dissolved oxygen levels showed the incursion of hypoxic waters in all the stations during the peak upwelling period. Upwelling signals were prominent in all the stations, but Mudbank and upwelling co-occurred at M2 alone during monsoon. The abundance of viruses ranged from 0.86 to 15.68 × 106 Viral-like Particles (VLPs mL-1) and prokaryotic abundance ranged from 2.73-16.26 × 105 cells mL-1. Viral and prokaryotic abundance was significantly higher in the monsoon compared to pre and late-monsoon. Based on Transmission electron microscopy (TEM) results, the non-tailed viruses constituted the major (43%) proportion of the total viruses in this study region. However, the viral production rates and viral-mediated bacterial mortality were high in the pre-monsoon compared to the monsoon and late-monsoon periods. There was no obvious effect of Mudbanks on viral dynamics and the observed variations in virological and hydrological features were governed mainly by coastal upwelling.

Hybrid-driven MRF seismic inversion for gas sand identification: A case study in the Yinggehai Basin

The Yinggehai Basin displays anomalies characterized by heightened levels of temperature and pressure. It represents a depositional model of a submarine fan with gravity-driven flow, demonstrating significant lateral reservoir heterogeneity and intricate spatial distribution of the gas reservoir. Identifying gas formations using elastic parameters as indicators relies on stable seismic inversion results. This requires regularization to alleviate ill-posedness in the inverse problem and to construct model features of the subsurface medium. Markov Random Field (MRF)is an effective soft-constrained regularization method. It enhances the marginal features of inversion results by penalizing the objective function with the gradient of neighborhood points. However, the standard MRF method relies only on the parameter model-driven and has poor applicability in areas with strong lateral inhomogeneity or complex depositional processes. In this research paper, we propose a novel approach to seismic inversion that integrates MRF neighborhood drive and incorporates both seismic data and a parametric model. Multi-order MRF neighborhoods are constructed by using seismic data in the horizontal direction (including horizontal diagonal) and parametric model data in the vertical direction (including vertical diagonal). The model-driven results are also utilized to couple seismic data and improve the stability of the hybrid-driven MRF inversion. In addition, we select the P-impedance as the parameter for inversion due to its heightened sensitivity towards gas formation within the study region. Consequently, we utilize the inversion results to delineate the presence of sandstone in the target layer and discern any indications of gas formation. The implementation of this method in the field has demonstrated its capability to enhance the stability of inversion outcomes, effectively integrating the lateral consistency of seismic data with the vertical precision of parametric model data. This approach significantly improves reservoir heterogeneity characterization and enhances accuracy in identifying sandstone and gas.

Integration of SPEI and machine learning for assessing the characteristics of drought in the middle ganga plain, an agro-climatic region of India.

Drought, as a natural and intricate climatic phenomenon, poses challenges with implications for both natural ecosystems and socioeconomic conditions. Evaluating the characteristics of drought is a significant endeavor aimed at mitigating its impact on society and individuals. This research paper explores the integration of the Standardized Precipitation Evapotranspiration Index (SPEI) and machine learning techniques for an assessment of drought characteristics in the Middle Ganga Plain, a crucial agro-climatic region in India. The study focuses on evaluating the frequency, intensity, magnitude, and recurrence interval of drought events. Various drought models, including Random Forest (RF), Artificial Neural Networks (ANN), and an ensemble model combining ANN and RF, were employed to analyze and predict drought patterns at different temporal scales (3-month, 6-month, and 12-month). The performance of these models was rigorously validated using key metrics such as precision, accuracy, proportion incorrectly classified, over-all area under the curve (AUC), mean absolute error (MAE), and root mean square error (RMSE). Furthermore, the research extends its application to delineating drought vulnerability zones by establishing demarcations for high and very high drought vulnerability areas for each model and temporal scale. Results indicate that the south-western part of the middle Ganga plain falls under the highly drought-vulnerable zone, which averagely covers 40% of the study region. The core and buffer regions of drought vulnerability have also been identified. The south-western part of the study area is identified as the core region of drought. Ground verification of the drought-vulnerable area has been done by using soil moisture meter. Validation metrics show that the ensemble model of ANN and RF exhibits the highest accuracy across all temporal scales. This research's findings can be applied to improve drought preparedness and water resource management in the Middle Ganga Plain. By identifying high-risk drought zones and utilizing accurate prediction models, policymakers and farmers can implement targeted mitigation strategies. This approach could enhance agricultural resilience, protect livelihoods, and optimize water allocation in this vital agro-climatic region.

Interannual variations of terrestrial water storage in the East African Rift region

Abstract. The US–German GRACE (Gravity Recovery and Climate Experiment, 2002–2017) and GRACE-FO (GRACE Follow-On, since 2018) satellite missions observe terrestrial water storage (TWS) variations. Over 20 years of data allow for investigating interannual variations beyond linear trends and seasonal signals. However, the origin of observed TWS changes cannot be determined solely with GRACE and GRACE-FO observations. This study focuses on the northern part of the East African Rift around the lakes of Turkana, Victoria, and Tanganyika. It aims to characterise and analyse the interannual TWS variations compared to meteorological and geodetic observations of the water storage compartments (surface water, soil moisture, and groundwater). We apply the STL (Seasonal-Trend decomposition using LOESS) method to decompose the signal into a seasonal signal, an interannual signal, and residuals. By clustering the interannual TWS dynamics for the African continent, we define the exact outline of the study region. We observe a TWS decrease until 2006, followed by a steady rise until 2016, and then the most significant TWS gain in Africa in 2019 and 2020. Besides meteorological variability, surface water storage variations in the lakes explain large parts of the TWS decrease before 2006. The storage dynamics of Lake Victoria alone contribute up to 50 % of these TWS changes. On the other hand, the significant TWS increase around 2020 can be attributed to nearly equal rises in groundwater and surface water storage, which coincide with a substantial precipitation surplus. Soil moisture explains most of the seasonal variability but does not influence the interannual variations. As Lake Victoria dominates the surface water storage variations in the region, we further investigate the lake and the downstream Nile River. The Nalubaale Dam regulates Lake Victoria's outflow. Water level observations from satellite altimetry reveal the impact of dam operations on downstream discharge and on TWS decreases in the drought years before 2006. On the other hand, we do not find evidence for an impact of the Nalubaale Dam regulations on the strong TWS increase after 2019.

Habitat suitability and relative abundance of wild boars in the east‐central Tianshan Mountains, China

AbstractAs wild boar populations and their distribution ranges increase, human–wild boar conflicts have become increasingly prevalent in numerous regions across the globe. These conflicts have a profound impact on human livelihoods, resulting in significant economic losses. Understanding the habitat requirements and relative abundance of wild boars is crucial prior to implementing any conservation measures. However, studies on wild boar habitat and population in the central and eastern regions of the Tianshan Mountains in China are lacking. We assessed the activity patterns and relative abundance of wild boars in these areas and evaluated habitat suitability using a combination of camera trapping, line transects, species distribution modeling (maximum entropy model), and hierarchical abundance modeling (Bayesian N‐mixture model). We used 311 infrared cameras and 280 field‐based line transects to cover approximately 31,000 km² from September 2022 to May 2023 in the east‐central Tianshan Mountains. We used 240 wild boar distribution locations and 13 environmental predictors in the development of species distribution models. We also used species counts and associated environmental predictors in the N‐mixture model to estimate the relative abundance of wild boar. Wild boars were most active during crepuscular hours (1800), and relatively active in the diurnal period compared to the nocturnal period. The probability of wild boar occurrence increased with higher normalized difference vegetation index (NDVI), the minimum temperature of the coldest month, and annual temperatures below 39°C. Boars were most likely to be found in closed deciduous‐coniferous forests. The relative abundance of wild boars was positively affected by NDVI and negatively affected by the minimum temperature of the coldest month and temperature annual range. Based on our results, we suggest areas of management priority. In particular, extensive and intact habitat with substantial wild boar populations, such as the Banfanggou, the South Mountain of Urumqi, and the Hutubi, should be prioritized for long‐term wild boar population monitoring and management so the adverse impacts of increasing wild boar populations in the study region can be minimized.

Drivers of plant biodiversity on cemeteries in the Ruhr Metropolitan Region (W Germany)

Cemeteries are biodiversity hotspots in urban areas, while the drivers of their floristic diversity are poorly understood. We investigated 153 cemeteries in the western Ruhr Metropolitan Region and generated comprehensive species lists of wild plants. Altogether, we found 964 species, equivalent to 53.6% of the flora known from the region. Cluster analysis revealed five distinct cemetery types based on their species composition and prevalence. Subsequently, we used ANOVAs to relate the most effective environmental descriptors identified with a CCA to the five clusters, thus revealing the drivers of species composition. Two of the main clusters had clear regional foci, which could be explained by soil types, while the third main cluster included most of the large cemeteries with many different habitat types, and encompassed sites from the entire study region. Here, the number of species is highest. Two smaller clusters included old historic and Jewish cemeteries, respectively, which were characterized by a smaller number of neophytes and heat indicators. Through the various functional types, the cemeteries contribute to the biodiversity of urban areas like the Ruhr Metropolitan Region and thus counteract the floristic homogenization of cities.

Multivariate statistical analysis as a tool for monitoring drinking water sources in an Atlantic Rainforest Conservation Unit.

Water quality monitoring is paramount in identifying and mitigating pollution sources, protecting aquatic ecosystems, and ensuring safe water for human and wildlife consumption. This study is aimed at evaluating the quality of drinking water sources in three communities located in a Sustainable Use Conservation Unit in the municipality of Mangaratiba, Rio de Janeiro, Brazil, employing a multivariate statistical analysis. A total of 161 water samples were collected from January to December 2022, encompassing 32 surface water and 129 tap water samples. Physicochemical parameters were determined in situ employing a Horiba U50 multiparameter probe. The samples were stored and transported at 4°C to the laboratory for microbiological analyses concerning total coliforms and Escherichia coli using a commercial enzymatic test. All samples contained coliforms, while E. coli were detected in 87% of the samples. The multivariate analysis indicated that the microbiological water quality in sampling region R2 was influenced by rainy periods and that, in general, the water quality within R3 was the most affected by the transport of solids to the water sources. The statistical methods applied herein aided in characterizing the study areas and detecting points of attention regarding physicochemical and microbiological parameters that significantly influence the water quality of each sampling point. Representative points for each study region were identified and may be employed for future monitoring and prevention actions.

A Systematic Review of the Current State of Numerical Groundwater Modeling in American Countries: Challenges and Future Research

In arid and semi-arid regions, groundwater is often the only available water source. However, overexploitation and pollution have led to a decrease in groundwater quantity and quality. Therefore, the proper management of groundwater resources is essential to promote sustainable development. Numerical simulation models (NSMs) have emerged as a valuable tool to address these challenges due to their ability to accurately and efficiently model groundwater systems. This study provides a comprehensive systematic review to evaluate the current knowledge on using numerical groundwater flow models for planning and water resource management in countries in the American region. A total of 166 research articles were published between the years 2000 and 2024. We analyzed and summarized details such as the study regions, numerical simulation methods and applied software, performance metrics, modeling units, modeling limitations, and prediction scenarios. In addition, we discuss alternatives to address the constraints and difficulties and suggest recommendations for future research. The continued research, improvement, and development of numerical groundwater models are essential to ensure the sustainability of groundwater resources.

Industrial Symbiosis and Circular Economy Practices Towards Sustainability in Forest-Based Clusters: Case Studies in Southern Brazil

This study aimed to investigate how Industrial Symbiosis (IS) built around thermoelectric power plants (TPPs) located in forest-based clusters in the southern plateau region of the state of Santa Catarina, Brazil, can stimulate the adoption of circular economy (CE) practices. Data were collected to identify the presence of forest-based clusters and the purchase and sale of logs, processed wood, and forest residues from companies within the clusters, and to investigate CE practices using the ReSOLVE framework adopted in two TPPs, which used residues and black liquor as fuel. The results indicate that the nature of the forest-based activities enhances the formation of clusters, especially when TPPs use forest residues for energy production with IS intensified in this process. Brazil has 140 TTPs that use forest biomass with the capacity to generate 5.5 million wK. Three clusters were identified in the study region with purchase and sale, respectively, of logs (78.6 and 100%), processed wood (75 and 66.7%), and wood residues (45.5 and 83.3%) within the region. The case studies indicated the strong occurrence of CE practices in the cases analyzed that contribute to four of the Sustainable Development Goals (SDG) proposed by the United Nations (7, 8, 9, and 12).

Planktonic to sessile: drivers of spatial and temporal variability across barnacle life stages and indirect effects of the Pacific Marine Heatwave

ABSTRACT Barnacles are a foundation species in intertidal habitats. During the Pacific Marine Heatwave (PMH), intertidal barnacle cover increased in the northern Gulf of Alaska (GoA); however, the role of pelagic larval supply in this increase was unknown. Using long-term monitoring data on intertidal benthic (percent cover) and pelagic larval populations (nauplii and cyprid concentrations), we examined potential environmental drivers (temperature, chlorophyll-a, mixed layer depth) of larval concentration and whether including larval concentration at regional and annual scales improved intertidal barnacle percent cover models in two study regions in the GoA. In both regions, larval concentrations were slightly higher following the PMH. Percent cover models were improved by including cyprid concentrations (but not nauplii), and the effect strength varied by site and tidal elevation. This indicates that larval concentration contributes as a bottom–up driver of benthic barnacle abundance. There is little evidence of a direct effect of the PMH on either life stage. Instead, our results may illustrate the positive feedback between life stages, where higher adult benthic abundance increased larval concentrations, which then supplied more new recruits to the benthos. As heatwaves continue to occur, integrating various data types can provide insights into factors influencing both benthic and pelagic communities.

Seafloor sediment characterization improves estimates of organic carbon standing stocks: an example from the Eastern Shore Islands, Nova Scotia, Canada

Abstract. Continental shelf sediments contain some of the largest stocks of organic carbon (OC) on Earth and play a vital role in influencing the global carbon cycle. Quantifying how much OC is stored in shelf sediments and determining its residence time is key to assessing how the ocean carbon cycle will be altered by climate change and possibly human activities. Spatial variations in terrestrial carbon stocks are well studied and mapped at high resolutions, but our knowledge of the distribution of marine OC in different seafloor settings is still very limited, particularly in dynamic and spatially variable shelf environments. This lack of knowledge reduces our ability to understand and predict how much and for how long the ocean sequesters CO2. In this study, we use high-resolution multibeam echosounder (MBES) data from the Eastern Shore Islands offshore Nova Scotia (Canada), combined with OC measurements from discrete samples, to assess the distribution of OC content in seafloor sediments. We derive four different spatial estimates of organic carbon stock: (i) OC density estimates scaled to the entire study region assuming a homogenous seafloor, (ii) interpolation of OC density estimates using empirical Bayesian kriging, (iii) OC density estimates scaled to areas of soft substrate estimated using a high-resolution classified substrate map, and (iv) empirical Bayesian regression kriging of OC density within areas of estimated soft sediment only. These four distinct spatial models yielded dramatically different estimates of standing stock of OC in our study area of 223 km2: 80 901, 58 406, 16 437 and 6475 t of OC, respectively. Our study demonstrates that high-resolution mapping is critically important for improved estimates of OC stocks on continental shelves and for the identification of carbon hotspots that need to be considered in seabed management and climate mitigation strategies.

A Validation of OLCI Sentinel-3 Water Products in the Baltic Sea and an Evaluation of the Effect of System Vicarious Calibration (SVC) on the Level-2 Water Products

The monitoring of coastal waters using satellite data, from sensors such as Sentinel-3 OLCI, has become a vital tool in the management of these water environments, especially when it comes to improving our understanding of the effects of climate change on these regions. In this study, the latest Level-2 water products derived from different OLCI Sentinel-3 processors were validated against a comprehensive in situ dataset from the NW Baltic Sea proper region through a matchup analysis. The products validated were those of the regionally adapted Case-2 Regional Coast Colour (C2RCC) OLCI processor (v1.0 and v2.1), as well as the latest standard Level-2 OLCI Case-2 (neural network) products from Sentinel-3’s processing baseline, listed as follows: Baseline Collection 003 (BC003), including “CHL_NN”, “TSM_NN”, and “ADG443_NN”. These products have not yet been validated to such an extent in the region. Furthermore, the effect of the current EUMETSAT system vicarious calibration (SVC) on the Level-2 water products was also validated. The results showed that the system vicarious calibration (SVC) reduces the reliability of the Level-2 OLCI products. For example, the application of these SVC gains to the OLCI data for the regionally adapted v2.1 C2RCC products resulted in RMSD increases of 36% for “conc_tsm”; 118% for “conc_chl”; 33% for “iop_agelb”; 50% for “iop_adg”; and 10% for “kd_z90max” using a ±3 h validation window. This is the first time the effects of these SVC gains on the Level-2 OLCI water products has been isolated and quantified in the study region. The findings indicate that the current EUMETSAT SVC gains should be applied and interpreted with caution in the region of study at present. A key outcome of the paper recommends the development of a regionally specific SVC against AERONET-OC data in order to improve the Level-2 water product retrieval in the region. The results of this study are important for end users and the water authorities making use of the satellite water products in the Baltic Sea region.

Identification of dominant drivers of streamflow spatiotemporal variations in typical mountainous areas in the Hexi Corridor, China

Study regionTypical mountain areas in the Hexi Corridor, China. Study focusWater security and ecosystem sustainability of arid inland river basins are highly dependent on upstream streamflow. However, due to the complex geographical environment and limited observation data in the study region, the attribution of spatiotemporal variations in streamflow influenced by climate change and/or human activities remains unclear. Here, we used partial least squares regression (PLSR) and the Budyko framework to unravel the dominant drivers of spatiotemporal variation in streamflow over the past 30 yr. New hydrological insight for the regionPrecipitation, topographic wetness index, slope, forest land, gross primary productivity, hydrological connectivity, soil organic carbon content, silt content, relative relief, NDVI and gravel content dominated spatial variation in streamflow. Temporal variation of streamflow was sensitive to precipitation and land surface. Specifically, increased precipitation and land surface alteration dominated the increase in streamflow in 50 % of the watersheds and the decrease in streamflow in 33 % of them, respectively. Further, land surface alteration was dominated by expansion of agricultural and built-up areas, weakened hydrological connectivity, increased landscape aggregation and forest cover. Controlling agricultural and built-up areas and the scale of afforestation, and focusing on the dynamics of hydrological connectivity and landscape patterns in the upstream reaches are imperative to maintain the security and sustainability of water resources in the arid inland river basins.