High Spatial Variability Research Articles

Hotspots of geogenic arsenic and manganese contamination in groundwater of the floodplains in lowland Amazonia (South America)

Arsenic enrichment in groundwater resources in deltas and floodplains of large sediment-rich rivers is a worldwide natural hazard to human health. High spatial variability of arsenic concentrations in affected river basins limits cost-effective mitigation strategies. Linking the chemical composition of groundwater with the topography and fluvial geomorphology is a promising approach for predicting arsenic pollution on a regional scale. Here we correlate the distribution of arsenic contaminated wells with the fluvial dynamics in the Amazon basin. Groundwater was sampled from tube wells along the Amazon River and its main tributaries in three distinct regions in Peru and Brazil. For each sample, the major and trace element concentrations were analyzed, and the position of the well within the sedimentary structure was determined. The results show that aquifers in poorly weathered sediments deposited by sediment-rich rivers are prone to mobilization and accumulation of aqueous arsenic and manganese, both in sub-Andean foreland basins, and in floodplains downstream. Two zones at risk are distinguished: aquifers in the channel-dominated part of the floodplain (CDF) and aquifers in the overbank deposits on the less-dynamic part of the floodplain (LDF). Some 70 % of the wells located on the CDF and 20 % on the LDF tap groundwater at concentrations exceeding the WHO guideline of 10 μg/L arsenic (max. 430 μg/L), and 70 % (CDF) and 50 % (LDF) exceeded 0.4 mg/L manganese (max. 6.6 mg/L). None of the water samples located outside the actual floodplain of sediment-rich rivers, or on riverbanks of sediment-poor rivers exceed 5 μg/L As, and only 4 % exceeded 0.4 mg/L Mn. The areas of highest risk can be delineated using satellite imagery. We observe similar patterns as in affected river basins in South and Southeast Asia indicating a key role of sedimentation processes and fluvial geomorphology in priming arsenic and manganese contamination in aquifers.

The role of natural and anthropic factors in the variability of soil loss in the state of Goiás between 1985 and 2018

Soil loss depends on natural and anthropic factors with high spatial and temporal variability, and can be inferred by predictive models such as the Revised Universal Soil Loss Equation (RUSLE). In this sense, the objective of this work is to analyze the spatio-temporal distribution and variation of soil losses in the state of Goiás for the years 1985, 2000 and 2018, based on the application of RUSLE and using the drainage basin as a multiscale unit of analysis. The results generally show that there has beenan increase in the average loss of soils in the state between the observed years with the average in 1985 being 2.4 ton.ha-1.year-1, growing to 10.8 ton.ha-1.year-1 in 2000, and 11.56 ton.ha-1.year-1 in 2018. The hydrographic regions of Tocantins and Paraná presented the highest losses. In addition to the general control of the Cerrado conversion expansion, the state of Goiás presents two spatial soil loss patterns; one in which the control of natural factors predominates, being linked to morphogenetic aspects with a strong influence of the topographic factor; and the other being rain erosivity, spatialized in areas such as the “front” of Cuesta Caiapó, Serra Dourada and Serra dos Pireneus. When associated, these two factors define sensitive areas with the highest soil losses in the state of Goiás.

Using Mobile Monitoring and Atmospheric Dispersion Modeling for Capturing High Spatial Air Pollutant Variability in Cities

Air pollution is still one of the biggest environmental threats to human health on a global scale. In urban environments, exposure to air pollution is largely influenced by the activity patterns of the population as well as by the high spatial and temporal variability in air pollutant concentrations. Over the last years, several studies have attempted to better characterize the spatial variations in air pollutant concentrations within a city by deploying dense, fixed as well as mobile, low-cost sensor networks and more recently opportunistic sampling and by improving the spatial resolution of air quality models up to a few meters. The purpose of this work has been to investigate the use of properly designed mobile monitoring campaigns along the streets of an urban neighborhood to assess the capability of an operational air dispersion model as SIRANE at the district scale to capture the local variability of pollutant concentrations. To this end, an IoT ecosystem—MONICA (an Italian acronym for Cooperative Air Quality Monitoring), developed by ENEA, has been used for mobile measurements of CO and NO2 concentration in the urban area of the City of Portici (Naples, Southern Italy). By comparing the mean concentrations of CO and NO2 pollutants measured by MONICA devices and those simulated by SIRANE along the urban streets, the former appeared to exceed the simulated ones by a factor of 3 and 2 for CO and NO2, respectively. Furthermore, for each pollutant, this factor is higher within the street canyons than in open roads. However, the mobile and simulated mean concentration profiles largely adapt, although the simulated profiles appear smoother than the mobile ones. These results can be explained by the uncertainty in the estimation of vehicle emissions in SIRANE as well as the different temporal resolution of measurements of MONICA able to capture local high concentrations.

Assessing the Information Potential of MIR Spectral Signatures for Prediction of Multiple Soil Properties Based on Data from the AfSIS Phase I Project.

The aim of the study was to assess the predictive potential of mid-infrared (MIR) spectral response in the estimation of 60 soil properties. It is important to know the accuracy limitations in estimating various soil characteristics using various models in conditions of high spatial variability of the environment. To fully assess this potential, three types of algorithms were used in modeling, i.e., partial least squares (PLSR), one-dimensional convolutional neural network (1DCNN), and generalized regression neural network (GRNN). The research used data from 19 sub-Saharan African countries collected as part of the Africa Soil Information Service (AfSIS) Phase I project. The repositories provide 18,250 MIR reflectance recordings and nearly two thousand analytical data records from the determination of many soil properties by reference methods. The modeled subset of these properties included texture (three variables), bulk density, moisture content at soil water characteristic curves (SWCC, 4 variables), total and organic C and total N content (3 variables), total elemental content (32 variables), elemental content in bioavailable forms (12 variables), electrical conductivity, exchangeable acidity, exchangeable bases, pH, and phosphorus sorption index. It is not possible to indicate a universal optimal prediction model for all soil variables. The best prediction results are provided by all regression models for total and organic C, total Fe, total Al and bioavailable Al content, and pH. For bulk density, total N and total K content satisfactory results are provided by specific model type. Many other properties, i.e., texture, SWCC, total Ga, Rb, Na, Ca, Cu, Pb, Hg content, and bioavailable Ca and K content, can be predicted with accuracies sufficient for some less demanding tasks.

Investigating the Spatial Structure of Soil Hydraulic Properties in a Long-Term Field Experiment Using the BEST Methodology

Understanding the spatial structure of soil properties at field scale and introducing this information into appropriate data analysis methods can help in detecting the effects of different soil management practices and in supporting precision agriculture applications. The objectives of this study were: (i) assessing the spatial structure of soil physical and hydraulic properties in a long-term field experiment; (ii) defining a set of spatial indicators for gaining an integrated view of the studied system. In seventy-two georeferenced locations, soil bulk density (BD), initial volumetric soil water content (θi) and cumulative infiltration curve as function of the time (I(t)) were measured. The soil water retention curve (θ(h)) and the hydraulic conductivity function (K(h)) were then estimated using the Beerkan Estimation of Soil Transfer parameters (BEST) methodology. The volumetric soil water contents at soil matrix (h = −10 cm), field capacity (h = −100 cm) and wilting point (h = −15,300 cm) were considered. In addition, a set of capacitive indicators—plant available water capacity (PAWCe), soil macroporosity (PMACe), air capacity (ACe) and relative field capacity (RFCe)—were computed. The data were first analyzed for overall spatial dependence and then processed through variography for structural analysis and subsequent spatial interpolation. Cross-correlation analysis allowed for assessing the spatial relationships between selected physical and hydraulic properties. On average, optimal soil physical quality conditions were recorded; only PMACe values were indicative of non-optimal conditions, whereas mean values of all the other indicators (BD, Ks, PAWCe, ACe, RFCe) fell within optimal ranges. The exponential model was found to be the best function to describe the spatial variability of all the considered variables, except ACe. A good spatial dependence was found for most of the investigated variables and only BD, ACe and Ks showed a moderate autocorrelation. Ks was confirmed to be characterized by a relatively high spatial variability, and thus, to require a more intensive spatial sampling. An inverse spatial cross-correlation was observed between BD and Ks up to a distance of 10 m; significant cross-correlations were also recorded between Ks and PMACe and ACe. This result seems to suggest the possibility to use these soil physical quality indicators as covariates in predictive multivariate approaches.

Assessment of LES of intermittent soot production in an aero-engine model combustor using high-speed measurements

Soot production in turbulent flames is an extremely intermittent phenomenon since it is the result of specific thermochemical conditions occasionally occurring in space and time. In realistic configurations such as the swirling flames used in gas-turbines, the presence of large-scale flow motions can additionally affect soot formation processes, leading to even more pronounced intermittency. Classically, the validation of numerical simulations is performed by comparing time-averaged results with experimental data of the phenomenon under investigation. This comparison can be considered as rigorous only if a statistically converged numerical representation is obtained. In case of sporadic events such as intermittent soot formation in turbulent flames, this means to perform the simulation over thousands of milliseconds of physical time, which can have extremely high CPU demands when performing Large Eddy Simulation (LES). In this work, a possible strategy to overcome this issue is proposed based on the use of high-speed measurements and numerically synthesized signals from LES. To illustrate the approach, numerical and experimental soot light scattering signals are considered here by looking at the model aero-engine combustor developed at DLR for the study of pressurized swirled sooting flames. The light scattering signal is numerically synthesized from an LES. Experimental high-speed measurements are used to statistically account for the high temporal and spatial variability of soot when considering time intervals similar to what is today achievable with LES. The feasibility of this approach is finally demonstrated by comparing numerical results to the ensemble of possible soot production states observed experimentally in the DLR burner allowing to eventually validate the present LES results.

Spatial variability of epikarst thickness and its controlling factors in a dolomite catchment

Epikarst, as a distinct layer of karst critical zone, plays a crucial role in water storage-transfer functions and biogeochemical processes. The epikarst thickness, which shows high spatial variability, is an important parameter that controls the dynamic characteristics of the water cycle. Due to the time-cost constraints in conventional field surveys and coarse resolution of remote sensing interpretation, empirical statistical models based on epikarst forming factors are an alternative to quantify the epikarst thickness. In this study, we investigated the spatial variability of epikarst thickness in Huanjiang, Guangxi of southwest China, based on electrical resistivity tomography with 36 sampling lines (1,658 sampling points) in a dolomite catchment. The controlling factors of epikarst thickness were defined based on selecting terrain-vegetation-soil variables with GLM (generalized linear model), RFE (recursive feature elimination) and PLSR (partial least squares regression) methods. Then, the best method for predicting epikarst thickness was selected based on the model with the highest accuracy. Results showed that the mean epikarst thickness was 6.32 m; the epikarst was significantly thicker in the depression (7.2 m) than on the hill slope (5.8 m), whereas there were no significant differences among slope positions. Geostatistical analysis showed that the spherical model reflected the strong spatial autocorrelation of epikarst thickness. Based on the prediction performance of the models, the terrain-vegetation variables alone could not explain epikarst thickness satisfactorily (explanation rate, 32.9 %; testing accuracy, R2 <0.5). By contrast, the modeling accuracy significantly increased (64.7 %, 0.7) when soil properties were considered as predictors. Based on validation criteria (R2 and RMSE), RFE for selecting variables and ANNs for prediction (0.814, 2.137 m) were the optimal model set to predict the epikarst thickness. This study provides a reference method for predicting epikarst thickness at a hillslope scale and the information would be helpful for hydrological catchment modeling.

Supraglacial debris thickness and supply rate in High-Mountain Asia

Supraglacial debris strongly modulates glacier melt rates and can be decisive for ice dynamics and mountain hydrology. It is ubiquitous in High-Mountain Asia, yet because its thickness and supply rate from local topography are poorly known, our ability to forecast regional glacier change and streamflow is limited. Here we combined remote sensing and numerical modelling to resolve supraglacial debris thickness by altitude for 4689 glaciers in High-Mountain Asia, and debris-supply rate to 4141 of those glaciers. Our results reveal extensively thin supraglacial debris and high spatial variability in both debris thickness and supply rate. Debris-supply rate increases with the temperature and slope of debris-supply slopes regionally, and debris thickness increases as ice flow decreases locally. Our centennial-scale estimates of debris-supply rate are typically an order of magnitude or more lower than millennial-scale estimates of headwall-erosion rate from Beryllium-10 cosmogenic nuclides, potentially reflecting episodic debris supply to the region’s glaciers.

Comparative Analysis of Pasture Soil Fertility in Semiarid Agro-Silvo-Pastoral Systems

Dryland pastures are usually on soils with a low fertility. However, a high spatial variability is apparent in pasture soils. In consequence, the application of inputs should be based on the particular characteristics of each field and their within-field variability. In this study, a comparative analysis of seven experimental fields (agro–silvo–pastoral ecosystems) in the southwest of the Iberian Peninsula was performed using a probabilistic and objective model. Some soil properties (the texture, cation exchange capacity, soil apparent electrical conductivity, organic matter, nitrogen, potassium and slope) measured at six sampling locations in each field were consolidated in the model. It was verified that the data fit the model and, later, a ranking of all the sampling locations and, consequently, a ranking of all the experimental fields according to the pasture soil fertility was established. Moreover, another output of the model was a ranking of the soil properties according to their influence on the soil fertility: the topographical property, the slope, was the most influential as it is related to the level of other important soil properties, as the textural components. Two fields, Cubillos and Grous, had the highest overall soil fertility levels because the soil samples in these fields were very fertile. However, some samples in the other five fields were also in the group of the most fertile, denoting the existence of an important spatial variability within each field. The proposed method is a rational way to improve the efficiency of the use of fertilisers by adjusting them to the soil pasture fertility.

What determines the success of rural water supply schemes? Insights from an assessment in Maharashtra

Abstract The article discusses a statistical model to explain the varying performance of water supply schemes under different physical environments in the state of Maharashtra in India, characterized by high spatial and temporal variability in rainfall and climate, and heterogeneity in geological and geomorphological conditions. The factors that enhance the performance of the schemes are effective utilizable recharge rates, extent of surface irrigation, and aquifer storage space. The factors that adversely affect the performance are the extent of the population covered by groundwater-based schemes and irrigation water demand per unit area. Subsequently, the districts in Maharashtra where groundwater-based schemes are likely to succeed were identified. The article also explains the practical and policy relevance of the model results for rural water supply planning.

The ecological diversification and evolution of Teleosauroidea (Crocodylomorpha, Thalattosuchia), with insights into their mandibular biomechanics.

Throughout the Jurassic, a plethora of marine reptiles dominated ocean waters, including ichthyosaurs, plesiosaurs and thalattosuchian crocodylomorphs. These Jurassic ecosystems were characterized by high niche partitioning and spatial variation in dietary ecology. However, while the ecological diversity of many marine reptile lineages is well known, the overall ecological diversification of Teleosauroidea (one of the two major groups within thalattosuchian crocodylomorphs) has never been explored. Teleosauroids were previously deemed to have a morphologically conservative body plan; however, they were in actuality morphofunctionally more diverse than previously thought. Here we investigate the ecology and feeding specializations of teleosauroids, using morphological and functional cranio-dental characteristics. We assembled the most comprehensive dataset to date of teleosauroid taxa (approximately 20 species) and ran a series of principal component analyses (PC) to categorize them into various feeding ecomorphotypes based on 17 dental characteristics (38 specimens) and 16 functionally significant mandibular characters (18 specimens). The results were examined in conjunction with a comprehensive thalattosuchian phylogeny (153 taxa and 502 characters) to evaluate macroevolutionary patterns and significant ecological shifts. Machimosaurids display a well-developed ecological shift from: (1) slender, pointed tooth apices and an elongate gracile mandible; to (2) more robust, pointed teeth with a slightly deeper mandible; and finally, (3) rounded teeth and a deep-set, shortened mandible with enlarged musculature. Overall, there is limited mandibular functional variability in teleosaurids and machimosaurids, despite differing cranial morphologies and habitat preferences in certain taxa. This suggests a narrow feeding ecological divide between teleosaurids and machimosaurids. Resource partitioning was primarily related to snout and skull length as well as habitat; only twice did teleosauroids manage to make a major evolutionary leap to feed distinctly differently, with only the derived machimosaurines successfully radiating into new feeding ecologies.

Spatial Microbial Respiration Variations in the Hyporheic Zones Within the Columbia River Basin

AbstractWhile the hyporheic zone (HZ) accounts for a significant portion of whole stream CO2 concentrations, HZ respiration modeling studies are lacking in quantifying their contributions to the total CO2 at large watershed/basin scales. Quantifying the contribution of anaerobic respiration is also underappreciated. This study used a carbon‐nitrogen‐coupled river corridor model to quantify HZ aerobic and anaerobic respiration and determined the key factors controlling their spatial variability within the Columbia River Basin (CRB). The modeled respiration patterns showed high spatial variability. Among the nine sub‐basins composing the CRB, the Lower Columbia and the Willamette, which receive higher precipitation, had higher respiration. Medium‐sized rivers (fourth to sixth orders) produced the highest aerobic and anaerobic respiration among reaches of different sizes. At the basin scale, aerobic respiration is dominant, representing approximately 98.7% of the total respiration across the CRB. While most of the reaches were dominant with aerobic respiration, reaches in agricultural land showed a relatively higher anaerobic respiration (18%) ratio. A variable importance analysis showed that hyporheic exchange flux controlled most of the spatial variability of HZ respiration, dominating over other physical variables such as residence time, stream dissolved organic carbon (DOC), nitrate, and dissolved oxygen (DO). The influence of substrate concentration (DOC and DO) is larger in modeling anaerobic respiration than aerobic respiration. Future efforts will focus on improving the estimation of the HZ exchange flux and the implementation of spatially explicit parameterizations for the reactions of interest to reduce model uncertainty.

High Spatial Variability of Biomass Burning Emissions Observed at Three Tibetan Plateau Sites

AbstractAerosol from biomass burning (BB) can affect regional and global climate by changing radiation balance and cloud properties, especially over the high altitude environ‐vulnerable region. To investigate the contributions of BB on atmospheric particulate matter trans‐the Tibetan Plateau (TP), the total suspended particle samples were collected at three high altitude sites over the northeastern (Qinghai lake, 3,200 m a.s.l.), central (Beiluhe, 4,659 m a.s.l.), and southwestern TP (Ngari, 4,360 m a.s.l.), respectively. Highly spatial variations of BB tracers (including levoglucosan, mannosan, and galactosan) were obtained. Elevated BB tracer concentrations were observed in the northeastern and southwestern sites compared to the central TP site. The heighten concentrations of particulate matter from biomass burning and organic carbon from biomass burning were obtained at Ngari (with 5260.3 and 2257.7 ng m−3) and Qinghai lake (with 1508.8 and 647.6 ng m−3) compared with those at Beiluhe (with 18.8 and 8.1 ng m−3), respectively. The results indicated that the BB contributions to aerosol were significant on the margin of the TP. In addition to the long‐range transport of BB pollutant, the local emissions may be an important contributor in the northeastern and southwestern TP. The present study can improve the understanding of BB characteristics and effects in the TP.

Mapping of mudflats with UAV images in La Bocana de Iscuandé, Colombian Pacific coast

Conservation planning for marine-coastal environments requires updated and detailed maps; the aim of this work is to generate detailed maps for two mudflats in the Iscuandé River Delta (Bocana de Iscuandé) in the Colombian Pacific Coast (Department of Nariño). These are important areas for fauna, especially migratory shorebirds. To address this problem, this study investigated the use of unmanned aerial vehicles (UAVs) as an alternative for collecting detailed mapping information in areas of difficult access using photogrammetric methodsand a geographic information system (GIS). UAV images were used to generate maps with a high level of detail at a scale of 1:10,000 that include detailed information about environments such as mudflats and mangroves which had not been mapped before. UAVs are a practical tool for mapping places where weather and access conditions hinder the use of satellite images or conventional aerial photography. They are also useful for mapping areas with high temporal and spatial variability, such as mudflats, where other platforms may not reveal their short-term dynamics. Our results show that this technique has a high potential for mapping such environments, and reveal that the Iscuandé mudflats aremaintained by a relative equilibrium between the ebb and flow of waves and tides, and are also influenced by the local climate. This type of application provides critical data for conservation and management strategies of areas with great ecological importance.

A machine learning approach for total electron content (TEC) prediction over the northern anomaly crest region in Egypt

In this paper, we developed an empirical model for predicting Total Electron Content (TEC) on a single station receiver located within the equatorial anomaly crest region at Helwan – Egypt (29.86°N, 31.32°E & MLAT 27.02°N). The neural network algorithm was developed from several inputs representing diurnal, seasonal, and solar cycle variations, solar and geomagnetic activity, and solar wind parameters using TEC data collected by the SCINDA GPS receiver. The TEC data collected for about nine years from 2009 to 2017 comprises almost the whole solar cycle 24 allowing the developed model to be validated and tested for different solar activity conditions. The 2009 – 2016 dataset was randomly divided into 70% for training, and 30% for validation during model development while the 2017 dataset was reserved for testing the model during independent validation. The validation was done by comparing TEC from the developed model and TEC derived from the GPS scintillation and TEC monitor (GPSTEC). Knowing that TEC modeling over the equatorial anomaly crest regions is challenging since it is characterized by high temporal and spatial variations, the developed model reconstructed diurnal GPSTEC from 2014 to 2016 with a root mean square error (RMSE) of 4.0 TECU and 3.2 TECU for high and low solar activity phases respectively. The neural network (NN) modeled TEC was highly correlated with GPSTEC, that is 0.93 and 0.94 for low and high solar activity respectively. In comparison, the International Reference Ionosphere (IRI)-2016 model predicted the general trend of GPSTEC in 2017 with RMSE of 5.43 TECU better than the developed TEC model with RMSE of 5.74 TECU. However, the NN model predicted diurnal GPSTEC for selected quiet and disturbed days, and seasonal TEC better than the IRI model in 2017. Therefore, the developed model was able to capture temporal ionospheric TEC for diurnal and seasonal variations during low solar activity. It was established that the performance of the empirical model depends on the size and distribution of the data used for model development. It’s worth noting that despite the improved performance of the NN algorithm in predicting diurnal and seasonal TEC variation, the NN-modeled TEC performed poorly in reconstructing the nighttime anomalies exhibited in the GPSTEC. The inclusion of the IRI-NmF2 as network input also adds to the improved performance of the network.

Hansbreen Snowpit Dataset – over 30-year of detailed snow research on an Arctic glacier

Snow cover is a key element in the water cycle, global heat balance and in the condition of glaciers. Characterised by high temporal and spatial variability, it is subject to short- and long-term changes in climatic conditions. This paper presents a unique dataset of snow measurements on Hansbreen, an Arctic glacier in Svalbard. The dataset includes 79 archived snow profiles performed from 1989 to 2021. It presents all available observations of physical properties for snow cover, such as grain shape and size, hardness, wetness, temperature and density, supplemented with organised metadata. All data has been revised and unified with current protocols and the present International Classification for Seasonal Snow on the Ground, allowing comparison of data from different periods and locations. The information included is essential for estimations of glacier mass balance or snow depth using indirect methods, such as ground-penetrating radar. A wide range of input data makes this dataset valuable to the greater community involved in the study of snow cover evolution and modelling related to glaciology, ecology and hydrology of glacierised areas.

Quantification of Water Resource Sustainability in Response to Drought Risk Assessment for Afghanistan River Basins

Drought in Afghanistan has many impacts from the interaction between environmental and socio-economic factors in the agriculture and water supply sectors. The complex topography and climate change impacts cause high spatial and temporal variation in the precipitation pattern and pose several challenges in managing water resources. Therefore, this paper aimed to quantify the sustainability of water resources against the progression of drought. Observed monthly precipitation data monitored by 23 stations located across five river basins covering the entire country for the period 1970–2016 were used to demonstrate the potential impacts of drought on water resources sustainability. Based on severe drought estimation, the year 2000 and 2001 were identified as change points. Accordingly, datasets pertaining to before and after the change points were partitioned to analyze the long-term temporal shift of drought. The spatiotemporal variability of sustainability indicators was estimated using reliability resiliency and vulnerability concept. The results indicated a positive trend of precipitation in most of the river basins after the change points leading to an increase in sustainability. However, the major portion in the upstream of the Kabul River basin showed a decrease in sustainability of around 15% due to a reduction in precipitation. As the Kabul city has high population density, it needs immediate attention in effectively planning and managing available water resources. Furthermore, the comprehensive analyses reported in this paper discuss the possible implications of drought risk assessment and its impact on largely varying sustainability both spatially and temporally in Afghanistan.

Perceptions of ecosystem services: Comparing socio-cultural and environmental influences.

Ecosystem services such as food provisioning, climate regulation, nutrient cycling, or recreation in open landscapes underpin human wellbeing. They are highly dependent on land use, land cover and utilization pattern as well as environmental factors like climate, topography and soil. In consequence, ecosystem services supply shows a high spatial variability. However, it is less clear if the perception of the importance of ecosystem services is similarly heterogeneous in space and amongst societal actors. The aim of this large-scale study was to explore whether land cover and climate gradients as well as socio-cultural factors influence the perceptions of ecosystem services of four groups of societal actors: citizens, farmers, foresters and nature managers. Spatially explicit survey data of 3018 respondents allowed to gain insight into the distribution of perceived importance of 21 ecosystem services in the federal state of Bavaria, Germany together with the respondents’ socio-cultural characterisation (e.g. gender, education and hobbies in nature). Responses were analysed through descriptive statistics, redundancy analysis, and Generalized Linear Models. Results reveal that the perceived importance of many ecosystem services was consistently high across groups, although perception differed for some ecosystem services (e.g. production of energy plants and timber as well as recreation in urban green space). Compared to other actor groups, farmers attributed slightly lower importance to all ES except provisioning services. Socio-cultural factors better explained variability in perceived importance of ecosystem services than land cover and climate gradients. This might be either explained by the fact that the environmental gradients vary not strong enough in our case study or that they do not shape the perceptions of respondents. A limitation of the study is that the sample of respondents obtained is not representative for the population, but biased towards persons interested in the topics of the survey. Still the consensus indicated by the overall positive perception of ecosystem services among respondents highlights the integrative potential of ecosystem services when included in decision-making.

Guided Filtered Sparse Auto-Encoder for Accurate Crop Mapping from Multitemporal and Multispectral Imagery

Accurate crop mapping is a fundamental requirement in various agricultural applications, such as inventory, yield modeling, and resource management. However, it is challenging due to crop fields’ high spectral, spatial, and temporal variabilities. New technology in space-borne Earth observation systems has provided high spatial and temporal resolution image data as a valuable source of information, which can produce accurate crop maps through efficient analytical approaches. Spatial information has high importance in accurate crop mapping; a Window-based strategy is a common way to extract spatial information by considering neighbourhood information. However, crop field boundaries implicitly exist in image data and can be more helpful in identifying different crop types. This study proposes Guided Filtered Sparse Auto-Encoder (GFSAE) as a deep learning framework guided implicitly with field boundary information to produce accurate crop maps. The proposed GFSAE was evaluated over two time-series datasets of high-resolution PlanetScope (3 m) and RapidEye (5 m) imagery, and the results were compared against the usual Sparse Auto Encoder (SAE). The results show impressive improvements in terms of all performance metrics for both datasets (namely 3.69% in Overal Accuracy, 0.04 in Kappa, and 4.15% in F-score for the PlanetScope dataset, and 3.71% in OA, 0.05 in K, and 1.61% in F-score for RapidEye dataset). Comparing accuracy metrics in field boundary areas has also proved the superiority of GFSAE over the original classifier in classifying these areas. It is also appropriate to be used in field boundary delineation applications.

Greenhouse Gas Emissions from a Main Tributary of the Yangtze River, Eastern China

Rivers and streams are recognized as potential greenhouse gas (GHGs: CO2, CH4, and N2O) sources, contributing to global warming. However, GHG emissions from rivers and streams have received insufficient attention compared to other ecosystems (forests, grasslands, wetlands, etc.). In this study, dissolved GHG concentrations were measured in the Qingyijiang River, the longest tributary in the lower reaches of the Yangtze River, during two campaigns in September 2020 and April 2021. Our results showed that the Qingyijiang River was oversaturated with dissolved GHGs. The dissolved GHG concentration in the surface river water ranged from 8.70 to 67.38 μM CO2, 0.03 to 2.06 μM CH4, and 12.30 to 32.22 nM N2O. The average diffusive GHG emission rates were 31.89 ± 22.23 mmol CO2 m−2 d−1, 697.22 ± 939.82 μmol CH4 m−2 d−1, and 18.12 ± 7.73 μmol N2O m−2 d−1. The total emissions (CO2-e) were CO2 (58%) dominated, while CH4 (38%) played a moderate role in total emissions. Temporally, average GHG concentrations and fluxes from the studied river in April were higher than those in September. The concentration and flux of CH4 exhibited high spatial variability, similar to those in most rivers. In contrast, we found that there was no obvious spatial variability in CO2 and N2O concentrations but a significant difference among reaches in N2O fluxes. We found that water temperature and flow velocity were the potential drivers for the regulating spatial variability in GHGs. However, no other observed limnological parameters were found in governing the spatial patterns of GHGs, suggesting a complex combination of factors governing GHG fluxes; thus, these inconspicuous mechanisms underscore the need for further research. Overall, our study suggests that this river acts as a minor source of GHGs relative to other rivers, and CH4 cannot be ignored when considering aquatic carbon emissions.