The Kashmir valley is prone to flooding due to its peculiar geomorphic setup compounded by the rapid anthropogenic land system changes and climate change. The study assesses the impact of land use and land cover (LULC) changes between 1980 and 2020 and extreme rainfall on peak discharge and sediment yield in the Upper Jhelum Basin (UJB), Kashmir Himalaya, India using KINEROS2 model. Analysis of LULC change revealed a notable shift from natural LULC to more intensive human-modified LULC, including a decrease in vegetative cover, deforestation, urbanization, and improper farming practices. The findings revealed a strong influence of the LULC changes on peak discharge, and sediment yield relative to the 2014 timeframe, which coincided with the catastrophic September 2014 flood event. The model predicted a peak discharge of 115101 cubic feet per second (cfs) and a sediment yield of 56.59 tons/ha during the September 2014 flooding, which is very close to the observed peak discharge of 115218 cfs indicating that the model is reliable for discharge prediction. The model predicted a peak discharge of 98965 cfs and a sediment yield of 49.11 tons/ha in 1980, which increased to 118366 cfs and, 58.92 tons/ha respectively in 2020, showing an increase in basin’s flood risk over time. In the future, it is anticipated that the ongoing LULC changes will make flood vulnerability worse, which could lead to another major flooding in the event of an extreme rainfall as predicted under climate change and, in turn compromise achievement of sustainable development goals (SDG). Therefore, regulating LULC in order to modulate various hydrological and land surface processes would ensure stability of runoff and reduction in sediment yield in the UJB, which is critical for achieving many SDGs.

For the soil in different regions, the nutrient fertility contained in it is different, and the detection and zoning management of soil nutrients before tillage every year can improve grain yield. In this paper, an integrated learning strategy model based on black soil hyperspectral data is designed for rapid classification of organic matter content classification of black soil. Soil hyperspectral image dataset of Xiangyang Experimental Base was collected; by changing the internal structure of the stacking model, an LSVM-stacking model with (MLP, SVC, DTree, XGBl, kNN) five classifiers as the L1 layer was built, and the simulated annealing algorithm was used for hyperparameter optimization. Compared to other stacking models, the LSVM-stacking metrics are significantly improved. The accuracy rate of hyperparameter optimization is improved by 38.6515%, the accuracy rate of the independent test data set is 0.9488, and the comparison of individual learners can improve the recognition classification accuracy of label"1" to 1.0.

Globally, seagrass meadows provide critical ecosystem services. However, seagrasses are globally degraded at an accelerated rate. The lack of information on seagrass spatial distribution and seagrass health status seriously hinders seagrass conservation and management. Therefore, this study proposes to combine remote sensing big data with a new hybrid machine learning model (RF-SWOA) to predict potential seagrass habitats. The multivariate remote sensing data is used to train the machine learning model, which can improve the prediction accuracy of the model. This study shows that a hybrid machine learning model (RF-SWOA) can predict potential seagrass habitats more accurately and effectively than traditional models. At the same time, it has been shown that the most important factors influencing the potential habitat of seagrass in the Hainan region were the distance from land (38.2%) and the depth of the ocean (25.9%). This paper provides a more accurate machine learning model approach for predicting the distribution of marine species, which can help develop seagrass conservation strategies to restore healthy seagrass ecosystems.

This study identified and mapped worldwide surveillance actions and initiatives of drinking water quality implemented by government agencies or public health services. The scoping review was conducted between July 2021 and August 2022 based on the Joanna Briggs Institute. The search was performed in relevant databases and grey literature; 49 studies were obtained. Quantitative variables were presented as absolute and relative frequencies, while qualitative variables were analyzed using the IRaMuTeQ software. The actions developed worldwide and their impacts and results provided four thematic classes: (1) assessment of coverage, accessibility, quantity, and drinking water quality in routine and emergency situations; (2) analysis of physical-chemical and microbiological parameters in public supply networks or alternative water supply solutions; (3) identification of household water contamination, communication, and education with the community; (4) and investigation of water-borne disease outbreaks. Preliminary results were shared with stakeholders to favor early knowledge dissemination.

The COVID-19 pandemic drew public attention back to the living conditions related to housing, access to green areas in close neighbourhood and nearby recreation. Several studies confirm that visiting green spaces improved the health and wellbeing in times of crisis. This representative study for the metropolitan area of Vienna, Austria’s capital, confirms the high relevance of contact with nature, particularly for citizens to cope with the negative consequences and perceived stress, anxiety, nervousness and many other negative symptoms experienced during the health crisis. It highlights the importance of nearby smaller but also medium to large scale green areas in cities for health and wellbeing of the population. Moreover, it specifically adds novel insights on age effects in use and perception of urban green spaces during COVID-19 pandemic. The online panel survey (n=1012) was conducted in summer 2021. In order to obtain a representative sample reflective of the population’s characteristics, a (stratified) random sample was selected by applying the quota method. The objective was to obtain a sample which represented gender, age and regional distribution of the population of the City of Vienna as well as the immediately surrounding local communities. Interestingly, particularly young people spent more time outside in green areas during the pandemic, discovered novel spaces for the first time and stated a high relevance of contact with nature for their wellbeing. Elderly people above sixty five, on the contrary, stayed more frequently at home. Significant differences are visible between the age groups also with regard to negative symptoms experienced in case of restricted access to green areas due to strict pandemic measures such as lockdowns and quarantine. All age groups, however, similarly acknowledged the importance of green areas for their physical, mental and emotional health in general during the pandemic.

The World Health Organization estimates that every year air pollution kills seven million people worldwide. As it is expected that climate change will affect future air quality patterns, the full understanding of the links between air pollution and climate change, and how they affect human health, are challenges of future research. In this scope, a methodology to assess the air quality impacts on health was developed. The WRF-CAMx modelling framework was applied for the medium-term future climate (considering the SSP24.5 scenario) and for the recent past (considered as baseline). Following the WHO recommendations, mortality health indicators were used to estimate health impacts of long-term exposures. For that, the Aveiro Region, in Portugal, was considered as a case study. Future climate results indicate the occurrence of higher temperatures, and lower total precipitation. Despite that, improvements in the main pollutants’ concentrations, and consequently in the reduction of the related premature deaths are foreseen, mainly due to the reduction of pollutants emissions imposed by the European legislation for the upcoming years. The applied approach constitutes an added value in this research field, being crucial to anticipate the effects of climate change on air quality and evaluate their impacts on human health.

Globally,coastal regions are vital areas of human activities and, as such, are centres of population growth and urban and economic development. Long-term human development has had a major impact on the ecological environment of coastal zones. Therefore, exploring the distribution and provenance of marine sediment types in coastal areas heavily influenced by human activities can provide scientific evidence and references for the current and future ecological management of these sensitive environments. For this reason, we have conducted an analysis on the sediment grain size, Endmember, organic matter content and geochemical elements in the Lu’erhuan River‒Malan Island‒Sandun Island area in the eastern part of Qinzhou Bay, a region heavily influenced by human activities. Sediment grain size clearly differs throughout the study site and the material provenances and hydrodynamic conditions are also varied, likely due to the local environmental conditions and the significant impact human activities have had on the area. The finest-grained sediment is imported from either inland or coastal areas via rivers and weak tidal currents, the next finest component is input from coastal areas through weak tidal currents, and the moderately coarse component mainly originates from nearby beaches. The two coarsest-grained sediment components are influenced by the combination of human activities, tidal currents and waves and enter the water via erosion. Organic matter provenance resembles that of the sediment components, exhibiting varied characteristics. This is due to the combination of natural and human activities in the bay. The organic matter in the upper reaches of the Lu’erhuan River originates from the river and coastal paddy fields, with obvious terrigenous characteristics; the organic matter in northern Malan Island mainly comes from external sources related to oyster farming; while organic matter in eastern Sandun Island is mainly produced endogenously by marine plankton. Al, Ti, Fe, Mg, K, Ga and other elements indicate that terrestrial sediments are significantly disturbed by human activities. However，Mn reflects the marine distribution of terrestrial sediments from the Lu’erhuan River to Jishuimen. Ca and Sr, indicators of marine sediments, are distributed in the eastern offshore area of Sandun Island, which is connected to the open waters. Due to the influence of human activities, As and Cd are highly enriched in the study area, while Cu is less affected by human activities.

We develop a machine learning (ML) approach for improving the accuracy of the horizontal dis-tribution of the aerosol optical depth (AOD) simulated by the CHIMERE chemistry-transport model over Northern Africa using Moderate-Resolution Imaging Spectroradiometer (MODIS) AOD satellite observations. These observations are used during the training phase of the ML method for deriving a regional bias correction of AODs simulated by CHIMERE. The results are daily maps of regional bias corrected AODs with full horizontal coverage over Northern Africa. We test four types of ML models: multiple linear regression (MLR), random forests (RF), gradi-ent boosting (XGB), and multiple layer perceptron networks (NN). We perform comparisons with satellite and independent ground-based observations of AOD that are not used in the training phase. They suggest that all models have overall comparable performances with a slight advantage of the RF model which expresses less spatial artifacts. While the method slightly underestimates the very high AODs, it significantly reduces biases and absolute errors, and clearly enhances linear correlations with respect to independent observations. This im-provement for deriving the AOD is particularly relevant for high dust pollution regions like the Sahara Desert, which dramatically lack ground-based measurements for validations of chemis-try-transport modeling which currently remains challenging and imprecise.

Observing cultivated crops and other forms of land use is an important environmental and economic concern for agricultural land management and crop classification. Crop categorization offers significant crop management data, ensuring food security, and developing agricultural policies. Remote sensing data, especially publicly available Sentinel 1 and 2 data, has effectively been used in crop mapping and classification in cloudy places because of their high spatial and temporal resolution. This study aimed to improve crop type classification by combining Sentinel-1 (Synthetic Aperture Rader (SAR)) data and the Sentinel-2 Multispectral Instrument (MSI) data. In the study, Random Forest (RF) and Classification and Regression Trees (CART) classier were used to classify grain crops (Barley and Wheat). The classification results based on the combination of Sentinel-2 and Sentinel-1 data indicated an overall accuracy (OA) of 93 % and a kappa coefficient (K) of 0.896 for RF and (89.15%, 0.84) for the CART classifier. It is suggested to employ a mix of radar and optical data to attain the highest level of classification accuracy since doing so improves the likelihood that the details will be observed in comparison to the single-sensor classification technique and yields more accurate results.

We investigated fault gases (helium, radon, CO₂) in two seismically active Cenozoic sedimentary environments: a) Meinweg (in 2015) at a tectonically quiet horst structure in the Lower Rhine Embayment and b) Bodanrück (in 2012; Lake of Constance) at the Molasse Basin and part of the Freiburg–Bonndorf–Bodensee Fault Zone (FBBFZ). Both study areas were selected because recent “GeoBio-Interactions” findings showed, that red wood ants (RWA) are biological indicators of otherwise undetected degassing systems. A total of 817 soil gas samples was analyzed. Currently, Meinweg can be considered “no ants land” due to the very low background-level geogas concentrations. Thus, anomalies (Rn-CO₂) weakly trending in NE-SW extension direction emerged. This could probably indicate the onset of (re)activation of the NE-SW trending (Variscan) structures or the development of new fractures as an aftershock process of the 1992 Roermond earthquake that occurred about 15 km to the west. Results at Bodanrück (3 RWA clusters and two RWA-free corridors) revealed degassing patterns in NW-SE and NNE-SSW directions in the clusters corresponding to re-activated and recent strike-slip fault systems. No gas anomalies were found in RWA-free corridors. The RWA nest distribution was shown to be a valuable tool for identifying areas of even actively degassing spotty anomalies caused by macro- and microscale brittle deformation masked by sediment cover.

Seasonal oscillations in the partial pressure of carbon dioxide (pCO2) in the Earth’s atmosphere stronger in northern latitudes are assumed to show that terrestrial photosynthesis exceeds respiration in summer reducing the pCO2, but increasing in winter when respiration exceeds photosynthesis. We disagree, proposing that variations in the temperature of the surface mixing zone of seawater also regulate the atmospheric pCO2, thermodynamically. We show that carbonate (CO32-) concentrations will therefore increase in summer with CaCO3 (calcite or aragonite) becoming less soluble, so calcium and carbonate ions are predicted to aggregate more while CO2 concentration falls in warmer seawater, thermodynamically favoring lower atmospheric pCO2. In winter, these physical processes are reversed, redissolving suspended calcite thus increasing carbonate alkalinity; carbonate concentration lessens as bicarbonate and soluble CO2 increase, raising the pCO2 in air. Our numerical computation shows that thermal fluctuations in equilibria favor absorption from air of more than one mole of CO2 per square meter in summer, coinciding with calcite formation maximizing in warmer water, potentially augmenting limestone reefs if there is a trend for increasing temperature . Another assumption we challenge is that upwelling from deeper water is the sole cause of increases in dissolved inorganic carbon (DIC) and alkalinity in surface waters, particularly in the southern hemisphere. Instead, calcite dissolution is favored as water temperature falls near the surface. It is well established that the seasonal summer decline in atmospheric CO2 coincides in fertile seawater with higher rates of biotic calcification and acidity, allowing increased CO2 capture by photosynthesis. However, its reversal in winter is proposed to be also a result of the cyclic dissolution of calcite as temperature falls, facilitated by biogenic respiration now exceeding photosynthesis; this can mutually provide the CO2 needed to convert carbonate ion alkalinity from calcite dissolution with bicarbonate increasing. Physical reasons why this oscillation is more obvious in the northern hemisphere include greater seasonal variations in water temperature (ca. 7.1 oC) being almost twice that in the cooler southern hemisphere (ca. 4.7 oC) and the greater depth of the surface mixing zone of seawater in the southern oceans. Evidence from 13CO2 fluxes between surface seawater and air is also assessed to test this hypothesis but questions remain regarding the regional rates of inorganic precipitation and dissolution of CaCO3 in the mixing zone. In summary, rapid biogenic calcification is favored by summer photosynthesis, but slower abiogenic calcification is more likely in warmer water.

Climate change is a phenomenon that makes the climate system of a given region to be more unpredictable and increases the risk of water-related problems. GCMs under the new CMIP6 framework holds several climate models with many improvements as compared to past similar efforts. The improvements are mainly in the number of scenarios formulated, setup, parametrization, and resolution. In this study, 10 downscaled climate models from CMIP6 are evaluated by applying statistical and data mining tools and are ranked based on their capability to describe the historical observed series. The result of the analysis showed that the outputs of the MPI-ESM1-2-HR model have a good overall ranking among those 10 models. The output of this top-ranked model is used to understand future climate over UASB after properly bias-corrected using the QM method. Results of the bias correction step show that average annual precipitation has shown an increment of 6.5\% in the middle (SSP2-4.5) and 10.3\% in the worst (SSP5-8.5) case scenarios for the mid-century (2040 - 2069). Similarly, for the end of the century (2070 - 2099) an increment of 4.7\% and 17.5\% was predicted for the two scenarios respectively. Whereas average annual maximum temperature series showed an increment of 1.5 $\degree C$ for middle and 2.6 $\degree C$ for the worst case in the mid-century. At the same time, an increment of 2.2 $\degree C$ and 3.5 $\degree C$ were predicted for the end of the century similarly for those scenarios. Furthermore, it was predicted that the average annual minimum temperature series will have an increment of 2.6 $\degree C$ and 3.1 $\degree C$ for mid-century and 3.1 $\degree C$ and 4.7 $\degree C$ for the end century for the two scenarios respectively. An increase in precipitation with increased land degradation problems in the sub-basin increases the risk of flood events in the future.

Sediment thickness increases can cause floodplains and the water level increases. This has the potential to generate a flood. Using electromagnetic waves, Time Domain Electromagnetic (TDEM) detected resistivity or conductivity contrast of lithology in the subsurface. It is measured in the time domain. TDEM method has been developing for decades. Here we tried to develop a 1-D forward modelling program for central loop configuration in the water environment using the Adaptive Born Forward Mapping (ABFM) method. We simulated this program in several water environment conditions (such as freshwater, brackish water and saline water) to know its response. Preventing natural hazards, especially flood hazards which are caused by the floodplain increases is our motivation in this research. Our simulation shows that Central-Loop Configuration Time-Domain Electromagnetic Method is able for imaging the sediment thickness clearly. The response of this method is extremely sensitive in saline water to depth changing than in other water environments.

Bayesian Maximum Entropy (BME) is increasingly used in predicting and mapping spatio-temporal data. However, studies that have fully evaluated its robustness empirically are rare. Therefore, this research examined empirically the effect of skewness, sample size and spatial dependency level using simulated data. We considered symmetric data, data positively skewed by 1, 3, 6 and 9, data with weak, moderate, and strong spatial dependency levels, and sample sizes from 100 to 500 at the interval length of 50. The results showed that the variation of sample sizes and spatial dependency levels do not affect the Mean Square Error (MSE) and bias of BME prediction. However, skewness affects the MSE of prediction but does not affect the bias. This result indicates that BME is robust to sample size and is unbiased. Despite the significant difference due to skewness, a graphical plot showed values of MSE close to zero, suggesting that BME can be considered robust to skewness.

To determine the metamorphic ages of the accretionary complexes in the Northern Chichibu Belt in SW Japan, K–Ar dating was conducted using weakly metamorphosed sedimentary rocks collected from the Kanto Mountains, Central Japan. Whole-rock ages were obtained for chert and red shale samples, and the mineral ages of fine-grained illite with the grain size less than 4 μm were obtained for chert, red shale, mudstone, acidic tuff, and basic tuff. The K–Ar ages of chert and red shale presented large variations, with systematically older ages compared to those of mudstone and tuff in the same strata. The influence of submarine hydrothermal activities on chert and red shale before subduction is a possible cause of this deviation. The illite samples, which were fractionated into four grain-size classes using a suspension method, yielded older ages and higher illite crystallinity (i.e., smaller values of Kübler’s crystallinity index) for larger grain-size classes. The peak metamorphic ages were determined from the K–Ar ages of the 3–4 μm class illite in mudstone and tuff. The Late Jurassic to the Earliest Cretaceous accretionary complex of the lowest structural unit (Kashiwagi Unit) was dated within a small range between 117–110 Ma, which is distinctly older than the K–Ar ages of white mica reported from the Sanbagawa Belt. The peak metamorphic age of acidic tuff (113 Ma) at the type locality of the Mikabu Greenstones indicates that the subducted Mikabu seamount is a constituent of the Kashiwagi Unit. The peak metamorphic ages of the Manba and Kamiyoshida Units were obtained as 132–107 Ma and 163–144 Ma, respectively. Major structural discontinuity is suggested within the Middle Jurassic accretionary complexes.

We developed an artificial neural network as an air quality model and estimated the scope of the impact of climate change on future (until 2064) summertime trends of hourly Ozone (O3) concentrations at an urban air quality station in Tehran, Iran. Our developed scenarios assume that present-time emissions conditions of O3 precursors will remain constant in the future. Therefore, only the impact of climate change on future O3 concentrations is investigated in this study. GCM projections indicate more favorable climate conditions for O3 formation over the study area in the future: the surface temperature increases over all months of the year, solar radiation increases, and precipitation decreases in future summers, and summertime daily maximum temperature increases about 1.2 ∘C to 3 ∘C until 2064. In the scenario based on present-time O3 conditions in 2012 summer without any axceedances, the summertime exceedance days of 8-hr O3 standard are projected to increase in the future by about 4.2 days in the short term and about 12.3 days in the mid-term. Similarly, in the scenario based on present-time O3 conditions in 2010 summer with 58 days of exceedance from 8-hr O3 standard, exceedances are projected to increase about 4.5 days in the short term and about 14.1 days in the mid-term. Moreover, the number of Unhealthy and Very Unhealthy days in 8-hr AQI is also projected to increase based on pollution scenarios of both summers.

Monsoon winds drive upwelling along the eastern coast of India during the south-west (SW) monsoons. These winds also provide alongshore windstress (AWS) resulting in positive cross-shore Ekman transport (ET) from March to the end of September. While instances of high ET and sea surface temperature based upwelling index (UI_SST) were observed along two parts of the coast: between Kashinagara and Kakinada in the north, and between Kavali and Point Calimere in the south. The UI_SST illustrated a much poorer agreement with local ET in the northern section, where the onset of UI_SST preceded the rise of ET and the subsidence of UI_SST signals occurred during a period of rising ET. Additionally, negative sea surface height anomalies (SSHAs), typically associated with coastal upwelling, were also missing through most of the upwelling period. A complex empirical orthogonal function (CEOF) analysis revealed two coherent modes of SSHA variation. The first mode showed a SSHA signature spatio-temporally coincident with the first upwelling and downwelling Kelvin waves closely associated with the equatorial zonal winds that drive them. The second CEOF mode, with a coastal SSHA pattern similar to the SSHA signatures of coastal upwelling, was associated with local offshore ET along the Indian east coast. The CEOF analysis exhibited the triggering of coastal upwelling in April and its suppression from June by coastally trapped Kelvin waves along the northeastern coast of India, while local AWS driven ET was the primary driver of coastal upwelling along the southeastern coast. The second CEOF mode also exhibited a coherent negative coastal SSHA signature excited by local AWS driven ET during the upwelling period. This study examined the spatio-temporal variability of premonsoon and SW monsoon coastal upwelling along the western Bay of Bengal and its relation to remotely forced coastal Kelvin waves.

A neglected dimension of the fire regime concept is fire patchiness. Habitat mosaics that emerge from the grain of burned and unburned patches (pyrodiversity) are critical for the persistence of a diverse range of plant and animal species. This issue is of particular importance in frequently burned tropical Eucalyptus savannas, where coarse fire mosaics have been hypothesized to have caused the recent drastic population declines of small mammals. Satellites routinely used for fire mapping in these systems are unable to accurately map fine-grained fire mosaics, frustrating our ability to determine whether declines in biodiversity are associated with local pyrodiversity. To advance this problem, we have developed a novel method (we call ‘double-differenced dNBR’) that combines the infrequent (c. bi-monthly) detailed spatial resolution Landsat with daily coarse scale coverage of MODIS and VIIRS to map pyrodiversity in the savannas of Kakadu National Park. We used seasonal Landsat mosaics and differenced Normalized Burn Ratio (dNBR) to define burned areas, with a modification to dNBR that subtracts long-term average dNBR to increase contrast. Our results show this approach is effective in mapping fine-scale fire mosaics in the homogenous lowland savannas, although inappropriate for nearby heterogenous landscapes. Comparison of this methods to other fire metrics (e.g., area burned, seasonality) based on Landsat and MODIS imagery suggest this method is likely accurate and better at quantifying fine-scale patchiness of fire, albeit it demands detailed field validation.

Waterlogging induced by rain in urban areas has a potential risk impact on property and safety. This paper focuses on the impact of rain on waterlogging and evaluates the waterlogging risk in the central city of Shanghai. A simplified waterlogging depth model is developed in different areas with different drainage capacity and rainfall in consumption of simplifying the effect of complex terrain characteristics and hydrological situation. Based on urban waterlogging depth and its classification collection, a Rain-induced Urban Waterlogging Risk Model(RUWRM) is further established to evaluate waterlogging risk in the central city. The results show that waterlogging depth is closely linked with rainfall and drainage, with a linear relationship between them. More rainfall leads to higher waterlogging risk, especially in the central city with imperfect drainage facilities. Rain-induced urban waterlogging risk model can rapidly gives the waterlogging rank caused by rainfall with a clear classification collection. The results of waterlogging risk prediction indicate that it is confident to get the urban waterlogging risk rank well and truly in advance with more accurate rainfall prediction. This general study is a contribution that allows the public, policy makers and relevant departments of urban operation to assess the appropriate management to reduce traffic intensity and personal safety or strategy to lead to less waterlogging risk.

Drought is considered one of the severest natural disasters and it is difficult to predict it. This review article aimed to display the state of the art of methods used to predict and monitor types of droughts. We examine more than 30 indices and models to identify the strengths and weaknesses of methods and identify gaps remaining in this field. Examples of examined indies are Palmer Drought Severity Index (PDSI), Standardized Precipitation Index (SPI), and Standardized Precipitation Evapotranspiration Index (SPEI). The research found improvement in drought modeling, however, more focus and improvement are required to monitor and predict drought types. It also found that some methods outperform others such as PDSI, SPI, SPEI, EVI, NDVI, NDWI, VCI and TCI.

The prediction of an extremely severe cyclonic storm (ESCS) is one of the challenging issues due to increasing intensity and its life period. In this study, an ESCS Fani that developed over Bay of Bengal region during 27 April - 4May, 2019 and made landfall over Odisha coast of India is investigated to forecast the storm track, intensity and structure. Two numerical experiments (changing two air-sea flux parameterization schemes; namely FLUX-1 and FLUX-2) are conducted with the Advanced Research version of the Weather Research and Forecasting (ARW-WRF) model by using a moving nest with fine horizontal resolution about 3 km. The high resolution (25 km) NCEP operational Global Forecast System (GFS) analysis and forecast datasets are used to derive the initial and boundary conditions, the ARW model initialized at 00 UTC 29 April 2019 and forecasted for 108 hours. The forecasted track and intensity of Fani is validated with available India Meteorological Department (IMD) best-fit track datasets. Result shows that the track, landfall (position and time) and intensity in terms of minimum sea level pressure (MSLP) and maximum surface wind (MSW) of the storm is well predicted in the moving nested domain of the WRF model using FLUX-1 experiment. The track forecast errors on day-1 to day-4 are ~ 47 km, 123 km, 96 km, and 27 km in FLUX-1 and ~54 km, 142 km, 152 km and 166 km in Flux-2 respectively. The intensity is better predicted in FLUX-1 during the first 60 h followed by FLUX -2 for the remaining period. The structure in terms of relative humidity, water vapor, maximum reflectivity and temperature anomaly of the storm is also discussed and compared with available satellite and Doppler Weather Radar observations.

Despite the abundance of tree diversity in the natural world, and generally high tree species richness in urban areas, urban forests continue to be dominated by a limited number of species. As socio-ecological systems, urban forests are shaped by historical and current management efforts and decision-making of a wide range of human actors. Drawing on past research, we offer a conceptual framework for describing the complex interactions among tree producers and consumers as trees are selected, grown, specified, and planted in private and public urban areas. We illustrate how multiple layers of selection criteria filter down the entirety of potential local tree diversity to a handful of commonly used and accepted tree species. We detail the actors and decision makers who impact tree composition and diversity across several land types. Finally, we identify research, education and outreach needs as they relate to creating more diverse and resilient urban forest ecosystems.

Climate and land use changes are two factors affecting the hydrology of watersheds. The greatest effect of climate change is probably the change in the hydrological cycle, thus water availability for human consumption, industry, and power generation. While the change in land use can cause changes in the hydrological components of the basin, such as evapotranspiration, runoff, and groundwater recharge. From these projections, it is necessary to understand the processes involved in climate change and land use and its impact on water systems. In this context, the objective of this study was to simulate climate change scenarios combined with the land use change and to analyze the impacts on the hydrology in the Piracicaba basin. Two periods were selected based on the preliminary result of detection of the abrupt change point of the annual flow series, using Petitt's non-parameter test. The first period selected prior to the occurrence of the point of the change was from 1985 to 2000. The other period after the point of the change was 2001 to 2015. Land use maps of the years 1990 and 2010 were used to represent the two periods. The results showed a good performance of the SWAT model. The NS (Nash-Sutcliffe coefficient) was 0.88 in calibration for both periods. In the validation, the NS was 0.92 and 0.94 for the first and second periods respectively. We then simulate climate and land use changes scenarios and compare them to the current scenario (baseline). The simulation results showed that the hydrological processes in the Piracicaba basin in the period 1985-2015, had more intense responses to land use change than climate.

Sea ice roughness can serve as a proxy for other sea ice characteristics such as ice thickness and ice age. Arctic-wide maps that represent spatial patterns of sea ice roughness can be used to better characterize spatial patterns of ice convergence and divergence processes. Sea ice surface roughness can also control and quantify turbulent exchange between sea ice surface and atmosphere and therefore influence surface energy balance at the basin scale. We have developed a data processing system that produces georeferenced sea ice roughness rasters that can be mosaicked to produce Arctic-wide maps of sea ice roughness. This approach starts with Top-of-Atmosphere radiance data from the Multi-angle Imaging SpectroRadiometer (MISR). We used red-band angular data from three MISR cameras (Ca, Cf, An). We created a training data set in which MISR pixels were matched with co-located and concurrent lidar-derived roughness measurements from the Airborne Topographic Mapper (ATM). We used a K-nearest neighbor algorithm with the training data to calibrate the multi-angle data to values of surface roughness and then applied the algorithm to Arctic-wide MISR data for two 16-day periods in April (spring) and July (summer). After georeferencing the roughness rasters, we then mosaicked each 16-day roughness dataset to produce Arctic-wide maps of sea ice roughness for spring and summer. Assessment of the results shows good agreement with independent ATM roughness data, not used in model development. A preliminary exploration of spatial and seasonal changes in sea ice roughness for two locations shows the ability to characterize the roughness of different ice types and the results align with previous studies. This processing system and its data products can help the sea ice research community to gain insights into the seasonal and interannual changes in sea ice roughness over the Arctic.

Stream chemistry effectively integrates watershed ecosystem processes on both spatial and temporal scales. Streams of coastal areas integrate a more homogeneous, flat topography wherein there can be interactions between the stream and the body of water into which it drains, especially where tidal fluxes occur. The present study assessed water quality of Thompson Bayou, which comes to the campus of the University of West Florida in a wetland after flowing through 4 km of commercial and private property with associated impacts on water quality. Sampling was carried out for one year at eight discrete sites along Thompson Bayou from the UWF campus to the Escambia River. We used a portable field meter to measure temperature, pH, dissolved O2 (DO), and specific conductivity (SC). Except for temperature, all variables exhibited a spatial pattern of significant variation with distance, with consistent increases in DO and SC as the stream approached the river. These variables also exhibited a temporal pattern of significant seasonal variation, including—and especially—temperature. Data suggest that spatial and temporal patterns of water quality of Thompson Bayou are determined by (1) processing of water by the wetland, (2) interactions of the stream channel with upland forest stands, and (3) the tidal hydrology of the Escambia River.

Data from ocean color (OC) remote sensing are considered a cost-effective tool for the study of biogeochemical processes globally. Satellite-derived chlorophyll, for instance, is considered an Essential Climate Variable since it is helpful in detecting climate change impacts. Google Earth Engine (GEE) is a planetary scale tool for remote sensing data analysis. Along with OC data, such tools allow an unprecedented spatial and temporal scale analysis of water quality monitoring in a way that has never been done before. Although OC data have been routinely collected at medium (~1 km) and more recently at high (~250 m) spatial resolution, only coarse resolution (≥4 km) data are available in GEE, making them unattractive for applications in the coastal regions. Data reprojection is needed prior to making OC data readily available in the GEE. In this paper, we introduce a simple but practical procedure to reproject and ingest OC data into GEE. The procedure is applicable to OC swath (Level-2) data and is easily adaptable to higher-level products. The results showed consistent distributions between swath and reprojected data, building confidence in the introduced framework. The study aims to start a discussion on making high resolution OC data readily available in GEE.

Spatial approach based on the deformation measurement of volcanic dome and crater rim is key to evaluate the activity of a volcano, such as Merapi volcano where associated disaster risk is regularly taking lives. Within this framework, this study aime to detect localized deformation and change in the summit area that has occurred concomitantly with the dome growth and explosion reported. The methodology was focused on two sets of data, one LiDAR-based dataset of 2012 and one UAV-dataset of 2014. The results show that during the period 2012-2014, the crater walls are 100 m to 120 m high above the crater floor at its maximum (North to East-South-East sector), while the West and North sector presents a topographic range of 40 to 80 m. During the period 2012 – 2014, the evolution of the crater rim around the dome is generally stable (no large collapse). The opening of a new vent on the surface of the dome has displaced an equivalent volume of 2.04 E+04 m3 corresponding to a maximum -9 m (+/- 0.9 m) vertically. This concludes that during the period 2012 – 2014 when the dome of Merapi experienced phreatic or phreatomagmatic explosions, the topography around the dome rose. This rise does not seem to be related to large wall collapses, and it is likely that modification in the subsurface have triggered those changes.

Paleoclimate information is key to understanding the climate system and predicting future climate. Currently, stable isotope analysis of whole molecules in tree rings is among the most advanced paleoclimate tools. Still, it only enables reconstructions of one climate parameter at a time (univariate climate reconstruction), a fundamental limitation. Here, we investigated whether this limitation can be overcome by analysing isotope variability at the level of intramolecular carbon positions in tree-ring glucose. We found that drought governs isotope variability at glucose C-1 to C-3, whereas radiation governs isotope variability at glucose C-5 to C-6. These isotope-climate relationships are statistically and mechanistically robust. According to cross-validation analysis, they can be reconstructed with high accuracy and precision. Thus, intramolecular isotope analysis enables multivariate climate reconstructions (proof of concept).

Abstract: This paper presents a comprehensive analysis of links between satellite-measured vegetation vigor and climate variables in Armenian mountain grassland ecosystems in years 1984–2018. NDVI is derived from MODIS and Landsat data, temperature and precipitation data are from meteorological stations. Two study sites were selected, representing arid and semi-arid grassland vegetation types, respectively. Various trend estimators including Mann-Kendall (MK) and derivatives were combined for vegetation change analysis at different time scales. Results suggest that temperature and precipitation had negative and positive impacts on vegetation growth, respectively, in both areas. NDVI-to-precipitation correlation was significant but with an apparent time-lag effect that was further investigated. No significant general changes were observed in vegetation along the observed period. Further comparisons between results from corrected and uncorrected data led us to conclude that MODIS and Landsat data with BRDF, topographic and atmospheric corrections applied are best suited for analyzing relationships between NDVI and climatic factors for the 2000-2018 period in grassland at a very local scale, but in the absence of correction tools and information, uncorrected data can still provide meaningful results. Future refinements will include removal of anthropogenic impact, and deeper investigation of time-lag effects of climatic factors on vegetation dynamics.

With the ever-increasing popularity of unmanned aerial vehicles and other platforms providing dense point clouds, filters for identification of ground points in such dense clouds are needed. Many filters have been proposed and are widely used, usually based on the determination of an original surface approximation and subsequent identification of points within a predefined dis-tance from such surface. We present a new filter, Multi-view and shift rasterization algorithm (MVSR) is based on a different principle, i.e., on the identification of just the lowest points in in-dividual grid cells, shifting the grid along both planar axis and subsequent tilting of the entire grid. The principle is presented in detail and compared both visually and numerically to other commonly used ground filters (PMF, SMRF, CSF, ATIN) on three sites with different ruggedness and vegetation density. Visually, the MVSR filter showed the smoothest and thinnest ground profiles, with ATIN the only filter performing comparably. The same was confirmed when comparing ground filtered by other filters with the MVSR-based surface. The goodness of fit with the original cloud is demonstrated by the root mean square deviations (RMSD) of the points from the original cloud found below the MVSR-generated surface (ranging, depending on site, between 0.6-2.5 cm). The MVSR filter performed outstandingly at all sites, identifying the ground points with great accuracy while filtering out the maximum of vegetation/above-ground points. The filter dilutes the cloud somewhat; in such dense point clouds, however, this can be perceived rather as a benefit than as a disadvantage.

The aim of this paper is to evaluate the surface concentration of nitrogen dioxide (NO2) inferred from the Sentinel-5 Precursor TROPOspheric Monitoring Instrument (S5P/TROPOMI) NO2 tropospheric column densities over Central Europe for two time periods, the summer of 2019 and the winter of 2019-2020. Simulations of the NO2 tropospheric vertical column densities and surface concentrations from the LOng-Term Ozone Simulation – EURopean Operational Smog (LOTOS-EUROS) chemical transport model are also applied in the methodology. More than two hundred in-situ air quality monitoring stations, reporting to the European Environment Agency (EEA) air quality database, are used to carry out comparisons with the model simulations and the space-borne inferred surface concentrations. Stations are separated into seven types (urban traffic, suburban traffic, urban background, suburban background, rural background, suburban industrial and rural industrial) in order to examine the strengths and shortcomings of the different air quality markers, namely the NO2 vertical column densities and NO2 surface concentrations. S5P/TROPOMI NO2 surface concentrations are inferred by multiplying the fraction of the satellite and model NO2 vertical column densities with the model surface concentrations. The estimated inferred TROPOMI NO2 surface concentrations are examined further with the altering of three influencing factors: the model vertical levelling scheme, the versions of the TROPOMI NO2 data and the air mass factors applied to the satellite and model NO2 vertical column densities. Overall, the inferred TROPOMI NO2 surface concentrations show a better correlation with the in-situ measurements for both time periods and all station types, especially for the industrial stations (R>0.6) in winter. The calculated correlation for background stations is moderate for both periods (R~0.5 in summer and R>0.5 in winter), whereas for traffic stations it improves in the winter (from 0.20 to 0.50). After the implementation of the air mass factors from the local model, the bias is significantly reduced for most of the station types, especially in winter for the background stations, ranging from +0.49% for the urban background to +10.37% for the rural background stations. The mean relative bias in winter between the inferred S5P/TROPOMI NO2 surface concentrations and the ground-based measurements for industrial stations is about -15%, whereas for traffic urban stations it is approximately -25%. In summertime, biases are generally higher for all station types, especially for the traffic stations (~ -75%), ranging from -54% to -30% for the background and industrial stations.

A time integration scheme based on semi-Lagrangian advection and Laplace transform adjustment has been implemented in a baroclinic primitive equation model. The semi-Lagrangian scheme makes it possible to use large time steps. However, errors arising from the semi-implicit scheme increase with the time step size. In contrast, the errors using the Laplace transform adjustment remain relatively small for typical time steps used with semi-Lagrangian advection. Numerical experiments confirm the superior performance of the Laplace transform scheme relative to the semi-implicit reference model. The algorithmic complexity of the scheme is comparable to the reference model, making it computationally competitive, and indicating its potential for integrating weather and climate prediction models.

Previous studies have shown, in the United States (U.S.), that non-White communities are exposed to significantly higher temperatures in urban environments than complementary White populations. Studies highlighting this disparity have usually been cross-sectional and are therefore “snapshots” in time. Using surface urban heat island (SUHI) intensity data, U.S. Census 2020 population counts, and a measure of residential segregation, this study performs a comparative analysis between census tracts identified as prevalent for White, Black, Hispanic and Asian populations and their thermal exposure from 2003 – 2018. The analysis concentrates on the top 200 most populous U.S. cities. SUHI intensity is shown to be increasing through time for all examined tracts. However, the increase is only statistically significant for White and Black prevalent zones. There is a 1.25K to ~2.00K higher degree of thermal exposure on average for non-White relative to White prevalent areas. When examined on an inter-city basis, White and Black prevalent tracts had the largest disparity, as measured by SUHI intensity, in New Orleans, LA, USA by < 6.00K. Hispanic (>7.00K) and Asian (<6.75K) prevalent tracts were greatest in intensity in San Jose, CA, USA. To further explore temporal patterns, two models were developed using a Bayesian hierarchical spatial temporal framework. One models the effect of varying the percentages of each population group relative to SUHI intensity within all examined tracts. Increases in percentages of Black, Hispanic, and Asian populations contributed to statistically significant increases in SUHI intensity. White increases in population percentage lowered SUHI temperature intensity. Throughout all modeled tracts, there is a statistically significant 0.01K per year average increase in SUHI intensity. A second model tests the effect of residential segregation on thermal inequity across all examined cities. Residential segregation, indeed, has a statistically significant positive association with SUHI intensity from this portion of the analysis. Similarly, there is a statistically significant 0.01K increase in average SUHI intensity per year for all cities. Results from this study can be used to guide and prioritize intervention strategies and furthers urgency related to social, climatic, and environmental justice concerns.

Biomass burning is an important and changing component of the global and hemispheric carbon cycles. Boreal forest fires in Russia and Canada are significant sources of greenhouse gases carbon dioxide (CO2) and methane (CH4). The influence of carbon monoxide (CO) on the greenhouse effect is practically absent: its main absorption bands of 4.6 and 2.3 μm are far away from the climatically important spectral regions. Meanwhile, CO concentrations in fire plumes are closely related to CO2 and CH4 emissions from fires. On the other hand, satellite measurements of CO are much simpler than those for the aforementioned gases. The Atmospheric Infrared Sounder (AIRS) provides a satellite-based CO data set since October, 2002 up to now. This communication presents estimates of CO emissions from biomass burning north of 30° N using a simple two-box mass-balance model. These results correlate closely with independently estimated CO emissions from the GFED4 bottom-up data base. Both ones reported record high emissions in 2021 throughout two decades, double the annual emissions comparing to the previous years. There have been two years with extremely high emissions (2003 and 2021), but for the rest of data upward trend with a rate of 3.6 ± 2.2 Tg CO yr-2 (4.8 ± 2.7% yr-1), was found. A similar rate of CO emission follows from the GFED4 data.

We present a method to study the interannual variability of the annual cycle. The method consists of first determining the amplitude and phase of segments of 12 monthly means at all spatial points, resulting in one complex number per grid point and per year. The complex fields, once per year, are then subject to a complex EOF (CEOF) analysis. We consider as an example the barotropic stream function in the South China Sea as simulated with an ocean general circulation model across 6 decades of years, driven my realistic (NCEP) weather forcing. We find 3 to 4 to “significant” CEOFs, which account for about 53 to 62% of variance. These CEOFs go with large-scale patterns. Their time coefficients are mostly stationary, but point to some inhomogeneities related to instationarities in the forcing. In particular, the simulation since 1950-1958 deviates from the remainder of the simulation. The first CEOF describes variations in the center of the South China Sea. Its principal component describes a systematic, albeit noisy shift by almost 180o from 1960 to about the year 2000. When overlaid the long-term mean annual mean, the overall change consists of an amplification of the annual cycle in the 1960s and 1990s, whereas In the 1970s, the amplitude was reduced. Phase shifts in the anomaly (given by the CEOFs) have a small effect, because of the dominance of the mean annual cycle. These variations are not related to ENSO variability but may origin in variations of the Southeast monsoon. The second EOF represents strong changes, both in terms of intensity and phase, in the Luzon strait.

A Water demand per capita will rise in the Arab world as a result of climate change and population expansion. One of the most important aims in coping with population increase around the world is to conserve water supplies. As a result, the Kingdom of Saudi Arabia constructed Al Wajeed Water Treatment System to meet the demands of the southern population. This research aims to assess the drinking water quality produced from the AlWajeed Water Treatment System. Monthly water samples were collected (January 2018 to January 2021) from the Al Wajeed Water Treatment Framework (4sites), extending to governorates; Bishah`s distribution system (5sites) and Tathleeth`s distribution system (7sites). Water quality criteria, such as physical, chemical, and microbiological parameters, revealed that the majority of water samples collected from the Al Wajeed Water Framework and its environs are of a good quality matched the national and international standards. Few sites showed water quality criteria, such turbidity, fluoride and total coliform did not comply with national and global standards. The obtained results explained the importance of monitoring and follow-up programs for drinking water criteria. In addition, they can help the authorities and stakeholders in the sustainable development.

Malaria is a constant reminder of the climate change impacts on health. Many studies have investigated the influence of climatic parameters on the of malaria transmission. Climate conditions can modulate malaria transmission through increased temperature, which reduces the duration of the parasite's reproductive cycle inside the mosquito. The intensity and frequency of the rainfall modulate the development of the mosquito population. In this study, the Liverpool Malaria Model (LMM) is used to simulate the spatio-temporal variation of the malaria incidence in Senegal. The simulations are based on the WATCH Forcing Data applied to ERA-Interim data (WFDEI) used as a point of reference, and biased-corrected CMIP6 models, separating historical and projections for 3 Shared Socio-economic Pathways scenarios (SSP126, SSP245 and SSP585). Our results highlight a strong increase in temperatures, especially towards eastern Senegal under the SSP245 but mainly the SSP585 scenarios. The ability of the LMM model to simulate the seasonality of malaria incidence is assessed. The model reveals a period of high malaria transmission between September and November with a maximum reached in October. Results indicate a decrease in malaria incidence in certain regions of the country for the far future and for the extreme scenario. This study is importance for the planning, prioritization, and implementation of control activities in Senegal.

The PB Sudirman hydroelectric power plant, built in 1988, has fewer than 50 years of operational life. In contrast, the planned operating life for this dam is 50 years. This dam is experiencing intensive sedimentation, which results in siltation. Dredging efforts and catchment area management are conducted to extend the operational life of the dam. Therefore, technical and economic analysis of dredging activities and the potential benefits of dredged sediment is needed to determine the solution's effectiveness. We use the site survey method to conduct an environmental impact analysis and interviews with dam managers. The identification of the extent and distribution of erosion-sedimentation was carried out as part of the technical assessment. In the economic study, we calculate the dredging costs and profits from selling dredged sediments, mainly sand. The analysis results show that the dredging of deposits in the foreset slope area is not profitable. There are 17 areas of bank spoil that have economic benefits. This study concludes that the need for dredging costs on foreset slopes is higher than the profit from selling dredging sediments.

Soil texture is key information in agriculture for improving soil knowledge and crop performance, so the accurate mapping of this crucial feature is imperative for rationally planning cultivations and for targeting interventions. We studied the relationship between radioelements and soil texture in the Mezzano Lowland (Italy), a 189 km² agricultural plain investigated through a dedicated airborne gamma-ray spectroscopy survey. The K and Th abundances were used to retrieve the clay and sand content by means of a multi-approach method. Linear (simple and multiple) and non-linear (machine learning algorithms with deep neural networks) predictive models were trained and tested adopting a 1:50,000 scale soil texture map. The comparison of these approaches highlighted that the non-linear model introduces significant improvements in the prediction of soil texture fractions. The predicted maps of the clay and of the sand content were compared with the regional soil maps. Although the macro-structures were equally present, the airborne gamma-ray data permits us shedding light on finer features. Map areas with higher clay content were coincident with paleo-channels crossing the Mezzano Lowland in Etruscan and Roman periods, confirmed by the hydrographic setting of historical maps and by the geo-morphological features of the study area.

The optical signals detected on multiple satellite platforms over snow surfaces are determined by the optical properties of snow surface and atmosphere. The solution of both direct and inverse problems of an atmosphere – underlying snow system requires simple relationships between top-of-atmosphere (TOA) reflectance R and microphysical/optical characteristics of both snow and atmosphere. The task of this paper is to present a simple analytical relationship between the value of R as detected on a satellite with atmosphere/snow properties. Such a relationship can be established using a numerical solution of integro - differential radiative transfer equation (RTE) (Liou, 2022). However, this path is quite complicated and time consuming. The analytical solutions of RTE are needed for the solution of various applied atmospheric and snow optics problems (Cachorro et al., 2022; Mei et al., 2020, 2022; Kokhanovsky, 2021). This is the main driver of this work. To simplify the problem under study we consider the case of Antarctica, where both snow and atmosphere are almost free of pollutants. This work is focused on the simulation of the moderate spectral resolution TOA measurements (1nm or so) and the spectral range 400-1000nm.

Integrated landscape approaches (ILA) aim to reconcile multiple, often competing, interests across agriculture, nature conservation, and other land uses. Recognized ILA design principles provide guidance for their implementation, yet application remains challenging, and a strong performance evidence-base is yet to be formed. A comprehensive literature review and focus group discussions with practitioners identified considerable diversity of ILA in actors, temporal, and spatial scales, inter alia. This diversity hampers learning from and steering these integrated planning approaches because of its intractable nature. Therefore, we developed a tool—an ‘ILA mixing board’—to structure the complexity of ILA into selectable and scalable attributes in a replicable way to allow planning, diagnostics, and comparative assessment of ILA. The ILA mixing board tool presents seven qualifiers, each representing a key attribute of ILA design and performance such as project flexibility, inclusiveness of the dialogue, and the centrality of the power distribution. Each qualifier has five (non-normative) outcome indicators that can be registered as present or absent. This process in turn guides planners, evaluators and other participating stakeholders involved in landscape management to diagnose the ILA type, and or its performance. We apply the ILA mixing board as a diagnostic tool to three ILA cases in Nicaragua, Madagascar, and the Congo Basin to show some of the many possible configurations of qualifiers on the mixing board. Overall, the tool allows comparative analyses of the complexity of ILA in a structured and manageable way.

In Qinglong ore field, paleo-oil reservoir is found to be associated with antimony deposits, and they have close genetic relationship. In this study, aromatics geochemistry of paleo-oil reservoir bitumen was studied to further discuss the thermochemical sulfate reduction (TSR) reaction and the mechanism of antimony mineralization. A total of 124 aromatic compounds were identified by Gas chromatography-mass spectrometry (GC–MS) analysis in bitumen samples, including abundant phenanthrene series, dibenzothiophene series, fluoranthene series, chrysene series, and a small number of fluorene series, naphthalene series, dibenzofuran series, biphenyl series, triaromatic steroid series. Aromatic parameters such as trimethylnaphthalene index (TMNr), methylphenanthrene index (MPI), methylphenanthrene distribution fraction (MPDF, F1 and F2), methyldibenzothiophene parameter (MDR), C28TAS-20S/(20R+20S), and benzofluoranthene/benzo[e]pyrene indicate Qinglong paleo-oil reservoir is in over maturity level. The abundance of phenanthrene and chrysene aromatic compounds and a small amount of naphthalene series, benzofluoranthene, fluoranthene, pyrene, anthracene, retene, perylene and biphenyl suggest that the parent material of the paleo-oil reservoir was mainly low aquatic organisms, mixed with a small amount of higher plant. The detected a certain number of compounds, such as retene, triaromatic steroid series and perylene, the ternary diagram of DBF–DBT–F and binary plot of Pr/Ph–DBT/P, DBT/(F+DBT)–DBF/(F+DBF) and Pr/Ph–DBT/DBF reveal that the source rock of the paleo-oil reservoir was formed in the marine environment of weak oxidation and weak reduction. The comprehensive analysis shows that the Qinglong paleo-oil reservoir originated from Devonian source rocks, just like other paleo-oil reservoirs and natural gas reservoirs in the Nanpanjiang basin. Abundant dibenzothiophene series were detected, indicating that the paleo-oil reservoir underwent a certain degree of TSR reaction. We believe that the paleo-gas reservoir formed by the evolution of paleo-oil reservoir participated in antimony mineralization, that is, hydrocarbon organic matter acted as reducing agent and transformed SO42− in oilfield brine into H2S through TSR, providing reduced sulfur and creating environmental conditions for mineralization.

Traditional soil characterization methods are time consuming, laborious and invasive and do not allow long-term repeatability of measurements. The overall aim of this paper was to assess and model spatial variability of the soil in an olive grove in south Italy by using data from two sensors of different type: a multi-spectral on-board drone radiometer and a hyperspectral visible-near infrared-shortwave infrared (VIS-NIR-SWIR) reflectance radiometer as well as sample data, to arrive at a delineation of homogeneous areas. The hyperspectral data were processed using continuum removal methodology to obtain information about the content and composition of clay. Differently, the multispectral data were firstly upscaled to the support of soil data using geostatistics and taking into account change of support. Secondly, the two-sensor data were integrated with soil granulometric properties by using the multivariate geostatistical techniques of multi-collocated cokriging and factor cokriging, in order to achieve a more exhaustive and finer-scale soil characterisation. The paper shows the impact of change of support on the uncertainty of soil prediction that can have a significant effect on decision making in Precision Agriculture. Moreover, four regionalised factors at two different scales (two per each scale) were retained and mapped. Each factor provided a different delineation of the field with areas characterised by different granulometry and clay composition. The applied method is sufficiently flexible and could be applied to any number and type of sensors.

Any change in the amount and annual distribution of rainfall causes a major socioeconomic and environmental problem where rainfed agriculture is predominant. For that reason, the objective of this study was to determine the long-term variability and trends of precipitation in the Wabi Shebele River Basin (WSRB), Ethiopia. The basin was discretized into 7 local agroclimatic zones (ACZ) based on annual rainfall and elevation. In this study, the coefficient of variation (CV) was used to check the variability of rainfall and modified Mann-Kendall (MK) and Innovative Trend Analysis (ITA) methods were used to detect rainfall trends. For each ACZ, stations with long-term records and less than 10 % missing data were selected for further analysis. The mean annual rainfall in the basin ranges from 227.2 mm to 1047.4 mm. The study revealed most of the ACZs showed a very high variation in Belg/Spring season rainfall (CV % > 30) than Kiremt/Summer and annual rainfall. Trend analysis revealed that no uniform trend was detected among ACZs at each temporal scale. But, most ACZ in the arid and semi-arid areas showed a non-significant decreasing trend. In comparison, a similar result was observed using MK and ITA methods.

Since last century, humanity has been using and disposing of resources at a greater rate than the Earth’s biocapacity to regenerate. Consequently, habitats are being destroyed, climate is changing and, for most, life conditions are deteriorating. To avoid collapse, humanity has been, at least in theory, trying to change the foundations of development so that it becomes ‘sustainable’ and, while meeting the social, economic and environmental needs of the present, does not compromise the ability of future generations to meet their own needs. To help on this task, in the 1960s, UNESCO proposed the establishment of laboratories for developing and testing sustainable approaches. These became known as Biosphere Reserves or, simply, biospheres. Today, Biosphere Reserves are considered to be the main instrument for testing and monitoring sustainable development approaches. By 2020, there were more than 700 biospheres in over 120 countries around the world - one third of which are located in prosperous countries with very-high human development levels. As of today, there is no mechanism to objectively measure the effectiveness of these reserves that also allows comparison between biospheres, their development approaches and outcomes, or over time. The objective of this work is to present a tool that fulfills these gaps and that, additionally, helps with establishing aspirational targets and identifying key areas that need improvement. The tool focuses on Biosphere Reserves located in countries with very-high levels of human development (but can be easily adapted to other countries); it addresses the 17 sustainable development goals and considers relevant international agreements. It is based on the rational that, to become widely used, it needs to be simple. Therefore, it uses data collected for other ends, hence available, and commonly used technology, such as excel. The tool consists of a spreadsheet that links a punctuating and a colouring system to topics, criteria, indicators and measures. The case study was on the Sunshine Coast region (Australia), which is in the process of being nominated a biosphere reserve.

A correctly obtained Land-use/land-cover (LULC) prediction map is essential to under-standing and assessing future patterns. In the study, the LULC map of Urmia/Iran in 2030 was produced using two different prediction methods CA-Markov and Artificial Neural Network (ANN). In general, the study followed a methodology consisting of three steps. In the first steps, Landsat satellite images acquired in 2000, 2010 and 2020 were classified with maximum likelihood algorithm and LULC maps were prepared for each year. In the second stage, to investigate the LULC prediction methods' validation (CA-Markov and ANN) the LULC prediction map of 2020 was produced using the LULC map of 2000 and 2010; In this step, the predicted LULC map of 2020 and the actual LULC map of 2020 were evaluated by correctness, completeness and quality indexes. Finally, The LULC map for 2030 was prepared using all two algorithms and the change map was extracted. The results show that the area of soil and vegetation decreased, and built-up regions increased during the research period. The methods validation results show that the two algorithms are much closer to each other. Nevertheless, in general, ANN has the highest completeness (96.21%) and quality (93.8%) and CA-Markov the most correctness (96.47). This study shows that the CA-Markov algorithm is most successful in predicting the future that had larger areas and a higher percentage in the region (urban and vegetation cover) and the ANN algorithm in predicting phenomena that had smaller levels with fewer percentages (soil and rock).

Airborne microplastic (MP) is an emerging pollutant, still under-characterised and insufficiently understood. Detailed description of MP air pollution is crucial as it has been identified in human lungs and remote locations, highlighting atmosphere as medium of MP dispersion and transportation. The lack of standardization of methods for measuring and further monitoring of the MP pollution is an obstacle towards the assessment of health risks. Since the first recognition of MP presence in the atmosphere of Krakow in 2019, this research was conducted to further characterise and develop the methods for qualitative and quantitative analysis of airborne MP (ATR-FTIR, Pyr-GC-MS, SEM-EDS) and pre-treatment of samples.The data was gathered in seven cycles, from June 2019 to February 2020. Methods used in the study allowed the identification and analysis of the changing ratio of the different types of synthetic polymers identified in the atmospheric fallout (LDPE, Nyl-66, PE, PET, PP, PUR). Observations of interactions between MP particles and environment were made with analyses of surface changes due to the degradation. Mineral phases attached to the MPs’ surfaces, with some of the inorganic contaminants transported on these surfaces, determined to also be of anthropogenic origin.Methodology proposed in this study, allows further characterisation of MP from multiple locations to provide highly comparable data, leading to the identification of the sources of this phenomenon, as well as seasonal changes.

Uranium (U) has been observed to be enriched in the waters of the southern Colorado Plateau including waters of the Navajo Nation. The region has naturally occurring U in rocks and a history of mining which may exacerbate U concentrations in waters. Despite past research into the abundance of U in the waters of the Navajo Nation, a regional framework has not been established to understand where U is typically higher and lower. We examined data collected by multiple studies and organizations to establish where and why U is likely to be enriched in waters of the Navajo Nation. Our work shows that U concentrations are related to the presence of U-rich rock bodies, elevation, and the salinity of local aquifers. Additionally, we show that U concentrations in water sources downstream from abandoned U mines are higher than they are in water sources that are not downstream from mines. Our work provides a means for understanding the patterns of U concentration in waters of the Navajo Nation and can act as a guide for local water withdrawal, as well as remediation and mitigation efforts for regional water sources.

The taxonomy of trilobites Abadiella Hupé, 1953 and Parabadiella Zhang, 1966 from the lower Cambrian of Gondwana has long been a controversy. Based on newly-collected and formerly-reported materials, 94 trilobite cranidia within 21 species are selected for morphometric analyses: principal components analysis (PCA) and canonical variates analysis (CVA). The integrated morphological characters are successfully accounted for by two principal components in PCA. The further validations for the presupposed qualitative groupings are indispensable to detect and calibrate the ultimate taxonomic results in CVA. By this way, all specimens distributed in a consistent morphospace in PCA and the short distances between them demonstrate their close morphological affinity, supporting their congeneric status within Abadiella. Additionally, three morphotypes were recognized from all selected specimens and they were eventually revised and incorporated into A. bourgini Hupé, 1953, A. huoi Zhang, 1966, and A. yunnanensis Luo, 1981, through stepwise test-calibration to get a high correct rate as far as possible in CVA. From this, it is certain that A. huoi is conspecific in South Australia and South China. Its occurrence allows to correlate the Australian A. huoi Range Zone to the identical zone in South China (in platform), having an approximately consistent stratigraphic range in the two regions. In comparison with A. huoi, A. bourgini was reported from fewer localities in South China. Nevertheless, its presence in the Daguinaspis Zone of Issendalenian Stage in Morocco permits a correlation with Chinese and Australian A. huoi Zone. As an auxiliary marker for A. huoi, the occurrence of A. bourgini in Morocco, South Australia and South China reinforce the correlation potential and resolution in species-level for the Cambrian Stage 3 in Gondwana region.

Documenting prehistoric and historical hemp retting for fiber extraction is important in the study of human uses of this iconic plant and its cultural implications. In paleoecology, hemp retting is usually inferred from indirect proxies, notably anomalously high percentages of Cannabis pollen in lake sediments, but some recent studies have also used specific molecular biomarkers (cannabinol, Cannabis DNA) as a more straightforward evidence. Here we provide direct evidence of hemp retting by identifying phylogenetic signatures (16S rRNA genes) from pectinolytic bacteria actually responsible for the fermentation process that separates the fiber from the stalk, namely Bacillus, Clostridium, Escherichia, Massilia, Methylobacterium, Pseusomonas, Rhizobium and Rhodobacter. These analyses have been performed in the sediments from an Iberian lake previously considered as an important hemp retting site during the last five centuries, on the basis of Cannabis pollen abundances. The good match between biomarker and pollen evidence, in the context of the recent historical development of hemp industry in Spain, can be useful to interpret paleoecological records from other similar lakes in the way toward a more regional view on the introduction, spreading, uses and associated cultural connotations of Cannabis in the Iberian Peninsula within European and Mediterranean contexts.

The classification of vessel types in SAR imagery is of crucial importance for maritime applications. However, the ability to use real SAR imagery for deep learning classification is limited, due to the general lack of such data and/or the labor-intensive nature of labeling them. Simulating SAR images can overcome these limitations, allowing the generation of an infinite number of datasets. In this contribution, we present a synthetic SAR imagery dataset with ship wakes, which comprises 46080 images for ten different real vessel models. The variety of simulation parameters includes 16 ship heading directions, 6 ship velocities, 8 wind directions, 2 wind velocities, and 3 incidence angles. In addition, we extensively investigate classification performance for noise-free, noisy, and denoised ship wake scenes. We utilize the standard AlexNet architecture and employ training from scratch. To achieve the best classification performance, we conduct Bayesian optimization to determine hyperparameters. Results demonstrate that the classification of vessel types based on their SAR signatures is highly efficient, with maximum accuracies of 96.16%, 92.7%, and 93.59%, when training using noise-free, noisy, and denoised datasets respectively. Thus, we conclude that the best strategy in practical applications should be to train convolutional neural networks on denoised SAR datasets. The results show that the versatility of the SAR simulator can open up new horizons in the application of machine learning to a variety of SAR platforms.

As the scarcity of potable water increases, recycling of treated wastewater is increasing. Small-scale, decentralized treatment can be implemented to serve local populations by keeping water within their boundaries and within reach for reuse, particularly in less dense, non-urban communities. Availability of cellular networks and high-speed internet connectivity, along with significant reduction in cost, allows ongoing monitoring of decentralized treatment systems at a central location. In this paper a decentralized treatment system using micro nanobubble aeration, moving media and membranes is able to produce reusable water with low energy consumption, thereby allowing the use of solar energy in places with unreliable electrical supply. The treatment system, which uses no external chemicals, is able to operate unattended and deliver clear, disinfected water with non-detect suspended solids, BOD5 less than 10 mg/L and nutrients below 5 mg/L.

The boreal Hemisphere has been experiencing increasing extreme hot and dry conditions over the past few decades, consistent with anthropogenic climate change expectations. The continental extension of the phenomenon calls for tools and techniques capable of monitoring the global to regional scales. In this context, the satellite is the only system that can satisfy the need for global coverage. The main objective we have addressed in the present paper is the capability of infrared satellite observations helping to monitor the vegetation stress due to increasing drought and heatwaves in summer. We have designed and implemented a new water deficit index (wdi) that exploits satellite observations in the infrared to retrieve humidity, air temperature, and surface temperature simultaneously. These three parameters are combined to provide the water deficit index. The index has been developed based on the Infrared Atmospheric Sounder Interferometer or IASI, which covers the infrared spectral range 645 to 2760 cm-1 with a sampling of 0.25 cm-1. The index has been used to study the 2017 heatwave, which hit Continental Europe from May to October. In particular, we have examined Southern Italy, where Mediterranean forests suffer from climate change. We have computed the index's time series and show that it can be used to indicate the atmospheric background conditions associated with meteorological drought. We have also found a good agreement with soil moisture, which suggests that the persistence of anomalously high water deficit index was an essential driver of the rapid development and evolution of the exceptionally severe 2017 droughts.

Short-term changes in shallow bathymetry affect the coastal zone and therefore their monitoring is an essential task in coastal planning projects. This study provides a novel approach for monitoring shallow bathymetry change based on drone multispectral imagery. Particularly we apply a shallow water inversion algorithm on two composite multispectral datasets being acquired five months apart in a small Mediterranean sandy embayment (Chania, Greece). Initially, we perform radiometric corrections using proprietary software and following we combine the bands from standard and multispectral cameras resulting in a six-band composite image suitable for applying the shallow water inversion algorithm. Bathymetry inversion results showed good correlation and low errors (< 0.3m) with sonar measurements collected with an uncrewed surface vehicle (USV). Bathymetry maps and true-color orthomosaics assist in identifying morphobathymetric features representing crescentic bars with rip channel systems. The temporal bathymetry and true-color data reveal important erosional and depositional patterns, which were developed under the impact of winter storms. Furthermore, bathymetric profiles show that the crescentic bar appears to migrate across and along-shore over the 5-months period. Drone-based multispectral imagery proves to be an important and cost-effective tool for shallow seafloor mapping and monitoring when it is combined with shallow water analytical models.

A better understanding of the expected future cyclonic activity, especially in the Mediterranean Basin in winter, is essential for developing scientifically based adaptation and mitigation methods to extreme precipitation and wind anomalies. The aim of this study is to analyze the change of winter cyclonic activity in the Mediterranean-Black Sea region, within the Atlantic–European region, at the beginning (as the recent historical period), middle and end of the 21st century. The projections are based on an ensemble of seven CMIP6 models, which showed the best consistency with NCEP/NCAR and ERA5 reanalysis, under the intermediate SSP2-4.5 and highest-emission SSP5-8.5 scenarios. The results show a consistent increase of the frequency of cyclones over Central Europe and the British Isles associated with the shift of cyclone tracks: norward from the Western Mediterranean region and southward from the Iceland Low. The latter leads to a decrease of the frequency in the north of the Atlantic–European region. At the same time, there is a reduction of the frequency of cyclones over the east of the Mediterranean Sea consistent with the decrease of cyclogenesis events. Area-averaged cyclone numbers in the Western and Eastern Mediterranean and the Black Sea subregions reduce to the end of the century under the highest-emission scenario, but not constantly and with a raise in the middle of the 21st century under both scenarios, which may be linked to the long-term multidecadal variability or regional features. In general, our study shows that the future winter cyclonic activity in the Mediterranean-Black Sea region responds unevenly to global climate changes, because regional and monthly features are important, as well as accounting for the long-term quasiperiodic variability.

The compelling effects of climate change on forests may have been underestimated in the past few decades in practical forestry. Although the first attempts to draw attention to this complex problem appeared almost half a century ago, the debate has been conceptual rather than experimental and applicative. At first glance, the con-cerns were mainly related to sustainable forest management (SFM) issues, which obviously needed attention. Over time, the effects of climate change have been mainly considered in the context of the SFM; they started from various and somewhat different scales and goals. Over time, more research and awareness of the im-portance of SFM under the pressure of climate change have led to the development of a clearer field that can be defined as ‘adaptive forest management’ - to climate change. One of the characteristics of this discipline is to be featured by the absence of univocal methods and / or objectives to be pursued but to identify, verify, and adapt methods to the various climatic and forest types and conditions found in the field. Therefore, this work shows some phases of forest planning and management concepts and criteria over time and recalls some innovative and / or adaptive methods related to the approach to forest planning and management under climate change

The detection of waste plastics in the marine and terrestrial environment using satellite Earth Observation data offers the possibility of large-scale mapping, and reducing on-the-ground manual investigation. In addition, costs are kept to a minimum by utilizing free-to-access Copernicus data. A Machine Learning based classifier was developed to run on Sentinel-1 and -2 data. In support of the training and validation, a dataset was created with terrestrial and aquatic cases by manually digitizing varying landcover classes alongside plastics under the sub-categories of greenhouses, plastic, tyres and waste sites. The trained classifier, including an Artificial Neural Network and post-processing decision tree, was verified using five locations encompassing these different forms of plastic. Although exact matchups are challenging to digitize, the performance has generated high accuracy statistics, and the resulting land cover classifications have been used to map the occurrence of plastic waste in aquatic and terrestrial environments.

Snowpack loss in midlatitude mountains is ubiquitously projected by Earth system models, though the magnitudes, persistence and time horizons of decline vary. Using daily downscaled hydroclimate and snow projections we examine changes in snow seasonality across the U.S. Pacific Southwest region during a simulated severe 20-year dry spell in the 21st century (2051–2070) developed as part of the 4th California Climate Change Assessment to provide a "stress test" for water resources. Across California’s mountains, substantial declines (30–100% loss) in median peak annual snow water equivalent accompany changes in snow seasonality throughout the region compared to the historic period. We find 80% of historic seasonal snowpacks transition to ephemeral conditions. Subsetting empirical-statistical wildfire projections for California by snow seasonality transition regions indicates a two-to-four-fold increase in burned area, consistent with recent observations of high elevation wildfires following extended drought conditions. By analyzing six of the major California snow-fed river systems we demonstrate snowpack reductions and seasonality transitions result in concomitant declines in annual runoff (47-58% of historic values). The negative impacts to statewide water supply reliability by the projected dry spell will likely be magnified by changes in snowpack seasonality and increased wildfire activity.

The compelling effects of climate change on forests may have been underestimated in the past few decades in practical forestry. Although the first attempts to draw attention to this complex problem appeared almost half a century ago, the debate has been conceptual rather than experimental and applicative. At first glance, the con-cerns were mainly related to sustainable forest management (SFM) issues, which obviously needed attention. Over time, the effects of climate change have been mainly considered in the context of the SFM; they started from various and somewhat different scales and goals. Over time, more research and awareness of the im-portance of SFM under the pressure of climate change have led to the development of a clearer field that can be defined as ‘adaptive forest management’ - to climate change. One of the characteristics of this discipline is to be featured by the absence of univocal methods and / or objectives to be pursued but to identify, verify, and adapt methods to the various climatic and forest types and conditions found in the field. Therefore, this work shows some phases of forest planning and management concepts and criteria over time and recalls some innovative and / or adaptive methods related to the approach to forest planning and management under climate change

This study examined the last four decades of the existing academic literature related to the environmental impacts of using cover crops in agricultural production systems. Data were collected from the Web of Science database, resulting in a sample of 3,246 peer-reviewed articles published between 1980 and 2021. We combined two advanced scientometrics analysis software (i.e., CiteSpace 6.0.R1 and Gephi 0.9.2) to identify the trajectory of the literature, hotspots, and frontiers. We developed authorship-, institution-and country-levels networks to examine academic cooperation over the last forty years. Our findings revealed that the number of peer-reviewed outputs documenting the environmental effects of cover crops has consistently increased, with a notable rise in publications between 2015 and 2021. Eighteen salient research topics were identified in the literature, including winter cover crops' effects on soil health, cover crops' effects on nitrous oxide emissions, and the relationship between cover crops and nitrate leaching. Based on the citation-clustering analysis, the trajectory of the literature may be divided into three stages. Studies in Stage 1_A (1980-2000) mainly assessed the role of cover crops in nitrogen management. In Stage 1_B (2001-2010), the research evaluated the impact of using different cover crop mixtures on farming systems. In Stage 2 (2011-2021), studies primarily addressed the environmental impacts of cover crops, particularly their effects on physical and chemical soil properties. Finally, the countries with the most outputs were the United States, Brazil, and Spain. The U.S. Department of Agriculture-Agricultural Research Service was the main contributor to the literature on the environmental impacts of cover crops.

Africa’s water needs are often supported by eutrophic waterbodies dominated by cyanobacteria posing health threats to riparian populations from cyanotoxins, and Lake Victoria is no exception. In two embayments of the lake (Murchison Bay and Napoleon Gulf), cyanobacterial surveys were conducted to characterize the dynamics of cyanotoxins in lake water and water treatment plants. Forty-six cyanobacterial taxa were recorded and out of these 14 were considered potentially toxigenic (i.e., from the genera Dolichospermum, Microcystis, Oscillatoria, Pseudanabaena and Raphidiopsis). A higher concentration (ranging from 5-10 µg MC-LR equiv. L-1) of microcystins (MCs) was detected in Murchison Bay compared to Napoleon Gulf, with a declining gradient from the inshore (max. 15 µg MC-LR equiv. L-1) to the open lake. In Murchison Bay, an increase either in Microcystis sp. biovolume and MC was observed over the last two decades. Despite high cell densities of toxigenic Microcystis and high MC concentrations, the water treatment plant in Murchison Bay efficiently removed the cyanobacterial biomass, intracellular and dissolved MC to below the lifetime guideline value for exposure via drinking water (&lt; 1.0 µg MC-LR equiv. L-1). Thus, the potential health threats stem from the consumption of untreated water and recreational activities, along the shores of the lake embayments. MC concentrations were predicted from Microcystis cell numbers regulated by environmental factors such as solar radiation, wind speed in the N-S direction and turbidity. Thus, an early warning through microscopical counting of Microcystis cell numbers is proposed to better manage health risks from toxigenic cyanobacteria in Lake Victoria.

Although the statistical methods of downscaling climate data have progressed significantly, the development of high-resolution precipitation products continues to be a challenge. This is especially true when interest centres on downscaling value over several study sites. In this paper , we report a new downscaling method termed the multi-site Climate Generative Adversarial Network (MSCliGAN), which can simulate annual maximum precipitation to the regional scale during the 1950-2010 period in different cities in Canada by using different AOGCM's from the Coupled Model Inter-Comparison Project 6 (CMIP6) as input. Auxiliary information provided to the downscaling model included topography and land-cover. The downscaling framework uses a convolution encoder-decoder U-net network to create a generative network and a convolution encoder network to create a critic network. An adversarial training strategy is used to train the model. The critic/discriminator used Wasserstein distance as a loss measure and on the other hand the generator is optimized using a summation of content loss on Nash-Shutcliff Model Efficiency (NS), structural loss on structural similarity index (SSIM), and adversarial loss Wasserstein distance. Downscaling results show that downscaling AOGCMs by incorporating topography and land-use/land-cover can produce spatially coherent fields close to observation over multiple-sites. We believe the model has sufficient downscaling potential in data sparse regions where climate change information is often urgently needed.

The paper concerns the distribution of apparent magnetic susceptibility in soil profiles located in the areas of topsoil magnetic susceptibility anomalies in Krakow. The type of land use, possible sources of magnetic carriers, and the type of soil were taken into account. Additionally, at each soil profile, a comparison between soil magnetic susceptibility and the results of geochemical analyzes of soil samples was made. The study shows very characteristic changes in magnetic susceptibility with depth, reflecting the interdependencies between natural and anthropogenic factors. A visible magnetic susceptibility maximum at the depth of 10-30 cm is observed at each soil profile. The maximum is associated mainly with the deposition of atmospheric dust and its vertical range depends on the level of anthropopression and natural conditions of soils. At the depth above 40 cm in the eastern part of Krakow, a correlation between the magnetic susceptibility and the soil type (chernozems de-veloped on loess) was found. All indicates that the thicknesses of contaminated upper horizons are not accidental and they depend on human interactions with the environment and the type of soil. An attempt at template establishment with the sources of magnetic particle carriers for different places in the city was made. As the result, in high urbanized sites, the extreme values of magnet-ic susceptibility rapidly change in short vertical distances can identify the richness of anthropo-genic layers with various types of anthropogenic ferrous material and/or additionally Fe-carrying objects buried in soils. In industrialized sites, anthropogenic input plays the most important role in the creation of soil magnetic characteristics. What is more, industrial pollution hides the natural magnetic properties of chernozems. In opposite, the studies at the sites under low anthropopression (mainly located in forests) allow for better insight into magnetic proper-ties arising during pedogenic processes, indirectly giving information about soil conditions. In the forest areas, the lowest values of soil magnetic susceptibility were measured. Additionally, the influence of pedogenic and lithogenic factors on forest soils is manifested in the results. Among the sites concerned, particular attention should be paid to the vicinity of the steel plant because of the agricultural land in the surroundings.

Land cover mapping provides spatial information on the physical properties of the Earth’s surface, for various classes of wetlands, artificial surface and constructions, vineyards, water bodies, etc. Having reliable information on land cover is crucial to developing solutions to a variety of environmental problems such as destruction of important wetlands/forests, and loss of fish and wildlife habitats. This has made land cover mapping one of the most widespread application areas in remote sensing computational imaging. However, due to the differences between modalities in terms of resolutions, content, and sensors, integrating complementary information that multi-modal remote sensing imagery exhibits into a robust and accurate system still remains challenging, and classical segmentation approaches generally do not give satisfactory results for land cover mapping. In this paper, we propose a novel dynamic deep network architecture, AMM-FuseNet, that promotes the use of multi-modal remote sensing images for the purpose of land cover mapping. The proposed network exploits the hybrid approach of the Channel Attention mechanism and Densely Connected Atrous Spatial Pyramid Pooling (DenseASPP). In the experimental analysis, in order to to verify the validity of the proposed method, we test AMM-FuseNet applied to four datasets whilst comparing it to the 6 state-of-the-art models of DeepLabV3+, PSPNet, UNet, SegNet, DenseASPP, and DANet. In addition, we also demonstrate the capability of AMM-FuseNet under minimal training supervision (reduced number of training samples) compared to the state-of-the-art, achieving less accuracy loss even for the case with 1/20 of the training samples.

Successful fertilization treatments targeted to improve stand productivity while reducing operational complexities and cost depend on a clear understanding of soil nutrient availability under varying environmental conditions. Soil nutrient data collected from 154 forest sites throughout the Inland Northwest, USA were analyzed to examine soil nutrient characteristics on different geologic soil parent materials and to rank soil fertility. Results show that soil parent material explains significant differences in soil nutrient availability. Soils developed from volcanic rocks have the highest CEC and are relatively high in P, K, S, Mg, Cu, Ca, and B, but generally poor in N. Forest soils developed from plutonic rocks exhibit the lowest CEC and are low in N, S, K, Mg, Cu, and Ca, but higher in P. Some soils located on mixed glacial till are low only in K, Cu, Mg, and Ca, but many glacial soils are relatively rich in other nutrients, albeit the second lowest CEC. Soils developed from metasedimentary and sedimentary rocks are among those with lowest soil nutrient availability for P and B. Sulfur was found to have the highest concentrations in metasedimentary influenced soils and the least in sedimentary derived soils. Our results should be useful in designing site-specific fertilizer and nutrient management prescriptions for forest stands growing on soils developed from these major geologies within the Inland Northwest region of the United States.

This study proposed a spatially and temporally improving methodology adopting the Regional Frequency Analysis with L-moments approach to estimate rainfall quantiles from 22787 grids of radar rainfall in Taiwan for a 24-hour duration. Due to limited radar coverage in the eastern region, significant discordant grids were found in the coastal area of the eastern region. A K-means cluster analysis using scaled at-site characteristics was used to group the QPESUMS grids in Taiwan into 22 clusters/sub-regions based on their characteristics. Spatially, homogeneous subregions with QPESUMS data produce more detailed homogeneous subregions with clear and continuous boundaries, especially in the mountain range area where the number of rain stations is still very limited. According to the results of z-values and L-moment ratio diagrams, the Wakeby (WAK), Generalized Extreme Value (GEV), and Generalized Pareto (GPA) distributions of rainfall extremes fitted well for the majority of subregions. The Wakeby distribution was the dominant best-fitted distribution, especially in the central and eastern regions. The east of the northern part and southern part of Taiwan had the highest extreme rainfall for each return period. Both areas were frequently struck by typhoons. By using grid-based (at-site) as the basis for assessing regional frequency analysis, the results show that the regional approach in determining extreme rainfall is very suitable for large-scale applications and even better for smaller scales such as watershed areas. The spatial investigation was performed by establishing regions of interest in small subregions across the northern part. It showed that regionalization was correct and consistent.

Snow is a vital environmental parameter and dynamically responsive to climate change, particularly in mountainous regions. Snow cover can be monitored at variable spatial scales using Earth Observation (EO) data. Long-lasting remote sensing missions enable the generation of multi-decadal time series and thus the detection of long-term trends. However, there have been few attempts to use these to model future snow cover dynamics. In this study, we therefore explore the potential of such time series to forecast the Snow Line Elevation (SLE) in the European Alps. We generate monthly SLE time series from the entire Landsat archive (1985-2021) in 43 Alpine catchments. Positive long-term SLE change rates are detected, with the highest rates (5-8 m/y) in the Western and Central Alps. We utilize this SLE dataset to implement and evaluate seven uni-variate time series modeling and forecasting approaches. The best results were achieved by Random Forests, with a Nash-Sutcliffe efficiency (NSE) of 0.79 and a Mean Absolut Error (MAE) of 258 m, Telescope (0.76, 268 m), and seasonal ARIMA (0.75, 270 m). Since the model performance varies strongly with the input data, we developed a Combined forecast based on the best performing methods in each catchment. This approach was then used to forecast the SLE for the years 2022-2029. In the majority of the catchments the shift of the forecast median SLE level retained the sign of the long-term trend. In cases where a deviating SLE dynamic is forecast a discussion based on the unique properties of the catchment and past SLE dynamics is required. In the future, we expect major improvements in our SLE forecasting efforts by including external predictor variables in a multi-variate modeling approach.

This brief review introduces some general properties of lower atmospheric motions that may be exploited by birds, linking historical findings and current knowledge from both observational and modeling perspectives. The relative importance of turbulent mixing resulting from wind shear and that from buoyant thermals is emphasized as well as the determinants of the geometry of the resulting vertical motions.

The establishment of thresholds is integral to environmental management. This paper introduces the use of thresholds in the context of deep-seabed mining, a nascent industry for which an exploitation regime of regulations, standards and guidelines is still in the process of being developed, and for which the roles and values of thresholds have yet to be finalised. There are several options for integrating thresholds into the International Seabed Authority’s regulatory regime, from being stipulated in regulations to being part of a mining contract, each option having its own advantages and disadvantages. Here we explore the range of ways that thresholds can be derived, set out the challenges in translating ecological and management data into thresholds, highlight factors for acceptance and operationalisation of thresholds in deep-seabed mining, and explain the necessity of refining thresholds as knowledge on impacts to features improves. Some comparable marine industries already use thresholds and these could potentially be used as starting points for the development of thresholds for deep-seabed mining. In order to be acceptable to the wide range of deep-seabed mining stakeholders, thresholds need to strike a balance among levels of harm acceptable by society, levels of environmental precaution justifiable by governments, scientific robustness, and operational practicality.

Early detection of wildfires has been limited using the sun-synchronous orbit satellites due to their low temporal resolution and wildfires’ fast spread in the early stage. NOAA’s geostationary weather satellites GOES-R can acquire images every 15 minutes at 2km spatial resolution, and have been used for early fire detection. However, advanced processing algorithms are needed to provide timely and reliable detection of wildfires. In this research, a deep learning framework, based on Gated Recurrent Units (GRU), is proposed to detect wildfires at early stage using GOES-R dense time series data. GRU model maintains good performance on temporal modelling while keep a simple architecture, makes it suitable to efficiently process time-series data. 36 different wildfires in North and South America under the coverage of GOES-R satellites are selected to assess the effectiveness of the GRU method. The detection times based on GOES-R are compared with VIIRS active fire products at 375m resolution in NASA’s Fire Information for Resource Management System (FIRMS). The results show that GRU-based GOES-R detections of the wildfires are earlier than that of the VIIRS active fire products in most of the study areas. Also, results from proposed method offer more precise location on the active fire at early stage than GOES-R Active Fire Product in mid-latitude and low-latitude regions.

The vulnerability of vegetation in the Middle East to meteorological conditions and climate change, especially those leading to drought, is high. Despite the importance of the Amu Darya and Kabul River Basins (ADB and KRB) as a region in which more than 15 million people live, and its vulnerability to global warming, only several studies addressed the issue of the linkage of meteorological parameters on vegetation for the eastern basins of Afghanistan. In this study, data from the Moderate Resolution Imaging Spectroradiometer (MODIS), Global Precipitation Measurement Mission (GPM), and Land Data Assimilation System (GLDAS) to examine the impact of meteorological parameters on vegetation for the eastern basins of Afghanistan for the period from 2000 to 2021. The study utilized several indices, such as Precipitation Condition Index (PCI), Temperature Condition Index (TCI), Soil Moisture Condition Index (SMCI), and Microwave Integrated Drought Index (MIDI). The relationships between meteorological quantities, drought conditions, and vegetation variations were examined by analyzing the anomalies and using regression methods. The results showed that the years 2000, 2001, and 2008 had the lowest vegetation coverage (VC) (56, 56, and 55% of the study area, respectively). On the other hand, the years 2010, 2013, 2016, and 2020 had the highest VC (71, 71, 72, and 72% of the study area, respectively). The trend of the VC for the eastern basins of Afghanistan for the period from 2000 to 2021 was upward. High correlations between VC and soil moisture (R = 0.70, p = 0.0004), and precipitation (R = 0.5, p = 0.008) were found, whereas no significant correlation was found between VC and drought index MIDI. It was revealed that soil moisture, precipitation, land surface temperature, and area under meteorological drought conditions explained 45% of annual VC variability.

Land Use Land Cover (LULC) change and urban growth have a significant influence on local climate of cities. From 1985 to 2021 the population of Baghdad increased by 103%. Therefore, the risen question is how this expansion influences the temperature of the city. The study aims to identify urban growth of Baghdad, investigate its influence on variation of Land Surface Temperature (LST) and identify the main factors that control the surface temperature of the city. Three Landsat images from 1985 to 2021, in addition to sixteen potential factors, were used in the study. Our findings suggest that during the study period, vegetated areas declined by 39% while built-up class increased by 139%. Bare soil recorded the highest surface temperature. The study found that surface temperature has a strong inverse relationship with vegetation (Normalized Difference Vegetation Index (NDVI): r = -0.62, p < 0.001) and moisture (Normalized Difference Moisture Index (NDMI): r = -0.65, p < 0.001). Therefore, increasing vegetation and water body lead to decrease temperature of the city. Our findings help policymakers to deal with climatic issues rising from urban growth of the city.

Forest road pavement needs an evaluation methodology based on a comprehensive assessment of road conditions. This research was conducted to evaluate the performance of a method for rating the surface condition of forest roads and eventually to adapt the method to the situation prevailing in a forest road network. The rating method selected as the basis for this experiment was the pavement condition index (PCI) developed by the U.S. Army Corps of Engineers. A 53 km of forest roads were selected contained the most influential factors and conditions variability. Eventually, 201 road segments were delineated between 150-300 m in length. Within the given segments, sample plots were set 20 m in length consecutively. It was concluded that the panel scores for distress and surface condition of sample unit and section differed from forest road pavement condition index (FRPCI) and PCI. Linear regression was used to derive equations between distress and PCI score to determine effective FRPCI parameters that provide a numerical rating for the condition of road segments within the road network, where 0 worlds are the worst possible condition, and 100 is the best possible condition best. Also, regression analysis showed the FRPCI model with a 0.87 correlation for the total of the road is a performance index used for the first time in forest roads. This study showed a range of FRPCI from 7.8 to 96.3, different from PCI and URCI ratings (0.85-45 and 1.2-53). The FRPCI index helps forest managers in road maintenance, harvesting, and planning to use road information.

With the renewed interest for lunar surface exploration, the European Space Agency envisions to stimulate the creation of lunar communications and navigation services (LCNS) to enable, among others, autonomous navigation capabilities for lunar rovers. As the number of satellites foreseen in such a service is much smaller compared to Earth based global navigation satellite systems (GNSS), different complementary technologies are pursued to improve the attainable navigation accuracy for lunar rovers. One way to improve the position accuracy provided by the LCNS satellites is to constrain the vertical position using a high resolution digital elevation model (DEM). This article presents the results of a variance covariance analysis of an extended Kalman filter (EKF) implementation in which the LCNS ranging measurements are used together with DEM from the LRO LOLA instrument. Assuming a realistic orbit determination and time synchronization (ODTS) accuracy of the LCNS satellites, the usage of a navigation grade IMU and an oven controlled crystal oscillator (OCXO), a 3-sigma position accuracy of less than 10 meters can be obtained. Furthermore, the availability is substantially improved as the DEM aided solution enables a position solution in case of only 3 visible satellites.

Groundwater degradation is a global problem linked to irrigation agriculture and aggravated by climate change. In drylands, where aquifer recharge is low, irrigation has emerged as an engine of economic growth. This problem falls under the paradigm of desertification, as it fits the definition of this complex problem in that the degradation of drylands is due to climatic variations and inappropriate human activities. Land Degradation Neutrality (LDN), the response of the United Nations Convention to Combat Desertification to the lack of progress in tackling desertification, is integrated into Sustainable Development Goal 15.3 and provides the adequate framework for implementing effective solutions. LDN prioritizes prevention strategies, and early warning systems coupled to integrated simulation models is a sound approach. We analyze the dynamics of a coastal aquifer in southern Spain, a “desertification landscape” according to the Spanish National Action Plan to Combat Desertification. For this purpose, we have (i) adapted a generic desertification model that considers socio-ecological systems as a particular case of predator-prey systems; and (ii) coupled to this model a risk analysis to calculate the probabilities of groundwater salinization under the current scenario of water resources use, driven by the expansion of greenhouse agriculture supported by external water transfer. The risk of desertification is close to 100%: groundwater salinity is 40 dS m-1 (well above tomato tolerance, 3.5 dS m-1), and 2.4 Mm3 yr–1 water transfer is needed to support the 631 ha of greenhouses. This worrying result suggests that complimentary solutions should be promoted to deactivate the ongoing process of desertification. Among them, we propose reclaimed water, diversifying the economy, or promoting crops adapted to aridity. This simulation framework shows how to explore the future of a socio-ecosystem under current scenarios and others that consider climate change, the energy crisis, or the impact of alternative solutions.

Severe windstorms resulting from mesoscale convective systems (MCS) cause significant disruption to society, including widespread power outages, tree and structural damage, and transportation accidents that affect multi-state regions and metropolitan areas along their track. A derecho, defined as a long-lived, widespread severe convective windstorm, is composed of numerous downbursts (intense localized storm downdrafts) organized into clusters or families of clusters. Derechos can produce winds above hurricane force along a track that may exceed several hundred kilometers. Convective windstorm potential has been expressed as a grouping of stability parameters that are relevant for downburst generation. These include lower-to-mid-tropospheric and equivalent potential temperature (θe) lapse rates, vertical relative humidity differences, and the amount of convective available potential energy (CAPE) in the troposphere. One major environmental factor addressed in the generation of widespread severe convective winds is an elevated mixed layer and its associated instability that promotes powerful storm updrafts and downdrafts. Another critical factor is developing a rear-inflow jet into an MCS that channels unsaturated mid-tropospheric air into the leading convective storm line. Establishing an elevated, ascending front-to-rear flow originating from deep, moist convection, overlying a strong and deep outflow-induced cold pool has been found to generate and sustain a robust rear inflow jet. The North American Derecho of 29-30 June 2012 exhibits many classic progressive and serial derecho features. It remains as one of the highest-impact derecho-producing convective systems (DCS) over CONUS since 2000. This research effort enhances the understanding of the science of operational forecasting of severe windstorms through examples of employing new satellite and ground-based microwave and vertical wind profile data. During the track of the derecho from the upper Midwestern U.S. through the mid-Atlantic region on 29 June 2012, clear signatures associated with a severe MCS were apparent in polar satellite imagery, especially from the EPS METOP-A Microwave Humidity Sounder (MHS), Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave Imager Sounder (SSMIS) and NASA TERRA Moderate Resolution Imaging Spectroradiometer (MODIS). In addition, morning (descending node) and the evening (ascending node) METOP-A Infrared Atmospheric Sounding Interferometer (IASI) soundings are compared to soundings from surface-based Radiometrics Corporation MP-3000 series microwave radiometer profilers (MWRPs) along the track of the derecho system. The co-located IASI and MWRP soundings revealed a pre-convective environment that indicated a favorable volatile tropospheric profile for severe downburst wind generation. An important outcome of this study will be to formulate a functional relationship between satellite-derived parameters and signatures, and severe convective wind occurrence. In summary, a comprehensive approach to observational data analysis involves both surface- and satellite-based instrumentation. Because this approach utilizes operational products available to weather service forecasters, it can feasibly be used for monitoring and forecasting local-scale downburst occurrence within derecho systems, as well as larger-scale convective wind intensity associated with the entire DCS.

The enormous increase in the volume of Earth Observations (EOs) has provided the scientific community with unprecedented temporal, spatial, and spectral information. However, this increase in the volume of EOs has not yet resulted in proportional progress with our ability to forecast agricultural systems.This study examines the applicability of EOs obtained from Sentinel2 and Landsat8 for constraining the APSIM-Maize model parameters. We leveraged leaf area index (LAI) retrieved from Sentinel2 and Landsat8 NDVI to constrain a series of APSIM-Maize model parameters in three different Bayesian multi-criteria optimization frameworks across 13 different sites across the U.S Midwest. A time variant sensitivity analysis was performed to identify the most influential parameters driving the LAI estimates in APSIM-Maize model. Then surrogate models were develop using random samples taken from the parameter space using Latin hypercube sampling to emulate APSIM’s behavior in simulating NDVI and LAI at all sites. Site-level, global and hierarchical Bayesian optimization models were then developed using the site-level emulators to simultaneously constrain all parameters and estimate the site to site variability in crop parameters. For within sample predictions, site-level optimization showed the largest predictive uncertainty around LAI and crop yield, whereas the global optimization showed the most constraint predictions for these variables. Lowest RMSE for within sample yield prediction was found for hierarchical optimization scheme (1423 Kg ha−1) while the largest RMSE was found for site-level (1494 Kg ha−1). In out-of-sample predictions within the spatio-temporal extent of the training sites, global optimization showed lower RMSE (1627 Kg ha−1) compared to the hierarchical approach (1822 Kg ha−1) across 90 independent sites in the U.S Midwest. On comparison between these two optimization schemes across another 242 independent sites outside the spatio-temporal extent of the training sites, global optimization also showed substantially lower RMSE (1554 Kg ha−1) as compared to the hierarchical approach (2532 Kg ha−1). Overall, EOs demonstrated their real use case for constraining process-based crop models and showed comparable results to model calibration exercises using only field measurements.

Due to the use of structural and seismic techniques an attempt has been made in this study to determine the hydrocarbon potential and future exploration in Pakistan's Lower Indus basin area and offshore region. In addition to the possibility of hydrocarbon accumulation, the major goals of this research are to analyze structural patterns in the subsurface of the study region, identify the horizons of various formations using surface seismic, and interpret seismic lines. Seismic data has been used to find and integrate the exposed Cretaceous/Tertiary basalt at Ranikot in the lower Indus basin to the well-known Deccan basalts of the Indo-Pak plate, which is the secondary goal of the current investigation. According to the interpretation of seismic data, the Indo-Pakistan plate has experienced rifting because of tectonic activity. Graben and horst structures have been identified in the study area horizon, which indicates that the area has undergone major structural and depositional changes. While faults provide a pathway for hydrocarbons to migrate from their source to reservoir rock, grabens are principally responsible for the accumulation of hydrocarbons. We further deduce that the prior exploration failures in the area were due to a lack of knowledge of the subsurface formations and structural trends, which are essential for characterizing the hydrocarbon play and trap features. Our results can be productive for the local hydrocarbon drilling projects as well as worldwide tectonic stratigraphy studies on passive continental edges.

Bangladesh is located in the alluvial floodplain of the Ganges–Brahmaputra–Meghna (GMB) river system and its numerous tributaries. In this country, the frequency, as well as severity, of riverbank erosion is high. To examine the impacts of riverbank erosion on rural food security, a baseline survey was conducted in Zajira, Bangladesh. The findings show that most people in the study area are affected by riverbank erosion. Moreover, agricultural production is gradually decreasing as a result of loss of farmland due to the catastrophe and its impacts. In some cases, people lose their entire homestead and are left with few income opportunities and low purchasing power. Consequently, they face the constant threat of a food crisis. The affected people follow some coping mechanisms to face these stressful situations, such as relying on less expensive or less preferred food items. While the local-government authorities have implemented some programmes to assist them, such as food aid and social-safety-net schemes, these are insufficient, due to the government's limited institutional resources and capacities. These findings suggest that for formulating effective rural-development plans, assessing the impact of riverbank erosion on the food security of people living in the affected areas is a prerequisite.

This paper concerns about a stepwise modelling procedure for groundwater flow simulation in a complex carbonate, folded and faulted, multilayer aquifer, in the Apennine chain in Central Ita-ly, which constitutes a source of good quality water for human consumption. A perennial river acts as the main natural drain for groundwater while sustaining valuable water related ecosys-tems. The spatial distribution of recharge was estimated using the Thornthwaite-Mather method on 60 years of climate data. The system was conceptualized as three main aquifers separated by two locally discontinuous aquitards. Three numerical models were implemented by gradually adding complexity to the model grid: single layer (2D), three layers (Quasi-3D), and five layers (Fully-3D), using an equivalent porous medium approach, in order to find the best solution with a parsimonious model setting. To overcome dry-cell problems in the Fully-3D model, the New-ton-Raphson formulation for MODFLOW-2005 was invoked. Calibration results show that a Fully-3D model was required to match the observed distribution of aquifer outflow to the river baseflow. The numerical model demonstrated the major impact of folded and faulted geological structures on controlling the flow dynamics in terms of flow direction, water heads and spatial distribution of the outflows to the river and springs.

The main target of this study was to evaluate the impact of future climate change on the available surface water resources in the Dhidhessa Sub-basin, Abbay Basin, Ethiopia. For the prediction, high-resolution Regional Climate Models (RCMs) from multiple climate models with data from Representative Concentration Pathways (RCPs) prepared by the CCAFS were used. The predictions of future discharge (stream flow) were based on climate scenarios data of baseline period of 1991 to 2020 and for the future with two time windows, 2044 (2030–2059) and 2084 (2070–2099), on a monthly time step after bias correction was conducted to both precipitation and temperature in the future climate under each RCP scenario. After sensitivity analysis, calibration (1994–2011), and validation (2012–2020) of the model for the Dhidhessa Sub-basin with the SUFI-2 program in the SWAT-CUP model, the SWAT model was used to determine water balance and stream flow from the SWAT model system. The SWAT model performed well in predicting stream flow in the Dhidhessa Sub-basins, with a coefficient of determination (R2) ranging from 0.8 to 0.94 and a Nash Sutcliffe value (NSE) ranging from 0.76 to 0.89. The percent decrease in mean annual stream flow from 2044 and 2084 were 10 %, in 2044, and 6.3% in 2084 respectively. The seasonal result under short-term 2044 of stream flow present change significantly decreased in spring, winter, and autumn with 20.2%, 67.4%, and 67.4% respectively. While summer season increased by 43.1%, under short-term 204. In long-term 2084 percent change declined except summer seasons, with 14.7%, 58.1%, and 3.3%, change in spring, winter, and autumn, respectively, and summer, with 51.1% increased and With RCP4.5 (1.64%) and RCP8.5 (2.1%) changed. In the future Dhidhessa stream flow increases and decreases from the baseline era. The decreasing stream flow in 2044 and 2084 will negatively affect agricultural production. This study revealed that any effect on this river resulting in a drop in flow will have a direct impact on ongoing water resource development and socio-economic development of the area.

Hydrological modeling for water management in large watersheds requires accurate spatially-distributed rainfall time series. In case of low coverage density of ground-based measurements, satellite precipitation products (SPP) constitute an attractive alternative, the quality of which must nevertheless be verified. The objective of this study was to evaluate, at different time scales, the reliability of six SPPs against a 2-year record from a network of 14 rainfall gauges located in the Ankavia catchment (Madagascar). The SPPs considered in this study are the African Rainfall Estimate Climatology (ARC2), the Climate Hazards group Infrared Precipitation with Station data (CHIRPS), the ECMWF Reanalysis (ERA5), the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and the African Rainfall Estimation (REF2) products. The results suggest that IMERG (R² = 0.63, slope of linear regression a = 0.96, root mean square error RMSE = 12 mm/day, mean absolute error MAE = 5.5 mm/day) outperforms other SPPs at the daily scale, followed by REF2 (R² = 0.41, a = 0.94, RMSE = 15 mm/day, MAE = 6 mm/day) and ARC2 (R² = 0.30, a = 0.88, RMSE = 16 mm/day, MAE = 6.7 mm/day). All SPPs, with the exception of the ERA5, overestimate the ‘no rain’ class (0 – 0.2 mm/day). ARC2, IMERG, PERSIANN, and REF2 all underestimate rainfall occurrence in the 0.2 – 150 mm/day rainfall range, whilst CHIRPS and ERA5 overestimate it. Only CHIRPS and PERSIANN could estimate extreme rainfall (>150 mm/day) satisfactorily. According to the Critical Success Index (CSI) categorical statistical measure, IMERG performs quite well in detecting rain events in the range 2-150 mm/day, whereas PERSIANN outperforms IMERG for rain events larger than 150 mm/day. Because it performs best at daily scale, only IMERG was evaluated for time scales other than daily. At the yearly and monthly time scales, the performance is good with R² = 0.97 and 0.87, respectively. At the event time scale, the probability distribution function PDF of rain gauge values and IMERG data show good agreement. However, at hourly time scale, the correlation between ground-based measurements and IMERG data becomes poor (R² = 0.20). Overall, the IMERG product can be regarded as the most reliable satellite precipitation source at monthly, daily and event time scales for hydrological applications in the study area, but the poor agreement at hourly time scale and the inability to detect extreme rainfall >200 mm/day may nevertheless restrict its use.

Hydrological modeling for water management in large watersheds requires accurate spatially-distributed rainfall time series. In case of low coverage density of ground-based measurements, satellite precipitation products (SPP) constitute an attractive alternative, the quality of which must nevertheless be verified. The objective of this study was to evaluate, at different time scales, the reliability of six SPPs against a 2-year record from a network of 14 rainfall gauges located in the Ankavia catchment (Madagascar). The SPPs considered in this study are the African Rainfall Estimate Climatology (ARC2), the Climate Hazards group Infrared Precipitation with Station data (CHIRPS), the ECMWF Reanalysis (ERA5), the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and the African Rainfall Estimation (REF2) products. The results suggest that IMERG (R² = 0.63, slope of linear regression a = 0.96, root mean square error RMSE = 12 mm/day, mean absolute error MAE = 5.5 mm/day) outperforms other SPPs at the daily scale, followed by REF2 (R² = 0.41, a = 0.94, RMSE = 15 mm/day, MAE = 6 mm/day) and ARC2 (R² = 0.30, a = 0.88, RMSE = 16 mm/day, MAE = 6.7 mm/day). All SPPs, with the exception of the ERA5, overestimate the ‘no rain’ class (0 – 0.2 mm/day). ARC2, IMERG, PERSIANN, and REF2 all underestimate rainfall occurrence in the 0.2 – 150 mm/day rainfall range, whilst CHIRPS and ERA5 overestimate it. Only CHIRPS and PERSIANN could estimate extreme rainfall (&gt;150 mm/day) satisfactorily. According to the Critical Success Index (CSI) categorical statistical measure, IMERG performs quite well in detecting rain events in the range 2-150 mm/day, whereas PERSIANN outperforms IMERG for rain events larger than 150 mm/day. Because it performs best at daily scale, only IMERG was evaluated for time scales other than daily. At the yearly and monthly time scales, the performance is good with R² = 0.97 and 0.87, respectively. At the event time scale, the probability distribution function PDF of rain gauge values and IMERG data show good agreement. However, at hourly time scale, the correlation between ground-based measurements and IMERG data becomes poor (R2 = 0.20). Overall, the IMERG product can be regarded as the most reliable satellite precipitation source at monthly, daily and event time scales for hydrological applications in the study area, but the poor agreement at hourly time scale and the inability to detect extreme rainfall >200 mm/day may nevertheless restrict its use.

Debris-flows are recurrent events on mountain- and hill- slopes, and they have been the object of numerous field investigations and sampling, however most of this work reposes on imagery and outcrop analysis, in such a way that there are still only a handful of studies investigating the internal architecture of these events’ deposits. In the present contribution, we aims at underneath the internal structure of a portion of a debris-flow deposit that was accessible in the aftermath of the 2018 heavy-rainfall debris-flows in Hiroshima Japan. Using a Ramac Pro-Ex GPR with 500 MHz and 800 MHz antenna, a set of longitudinal and transversal transects was used to characterize the deposit. The results demonstrated that a set of subhorizontal layers have filled the valley, and interacting with local terrace edges, these layers have piled up and overcome the obstacle. Across the valleys, a set of trough suggest the presence of channels that were also filled during the event. Finally, in the channel post-event, a set of radargram “cross-bedded units” shows that the final deposition in the channel was of a more dilute flow, typical of a Newtonian flow. This set of units was not to be found at the surface of the post-event pseudo-surface, suggesting that the flow ended as a debris-flow on this surface, and that it is only when the flow dug the final channel that the nature of the flow returned from debris-flow to Newtonian flow.

Nations of the world have seen unprecedented changes in climate variables in recent decades. But it is unclear to what extent climate change has impacted and will impact food systems in some developing regions, and how policymakers can frame an approach to encouraging adaptation and advancing climate-smart agriculture. Many studies attempting to link agroecology to climate change adaptation do so without understanding the potential of Agroecology not only to mitigate climate change – which is the weak response – but to reverse its impact and ‘climate proof’ our food systems. By modeling the near and far future impacts of climate change on crop production, we showed how climate will impact crop production under two crop production systems (agroecology and non-agroecology production systems). The overarching aim is to derive sustainable development strategies and lessons for policymakers and climate researchers - essential components of environment and Agricultural development. Using case studies from Nigeria, we observed that transitioning to agroecology, even at the farm level also transforms farm designs, thereby affecting their overall food and nutrition status. The result showed that the use of agroecology management practices not only reduces the impact of climate change in the near future but will also lead to increased crop yield in the future. The finding suggests that to feed the over 400 million projected population of Nigeria by 2050, the use of agroecological practices will be a better alternative to the conventional farming methods. To advance the use of agroecological farming methods, governments at every level in Nigeria need to mainstream organic agriculture in national government policies. This is important as it will not only address climate change impacts but also hunger and poverty.

Accurately predicting ambient dust plays a crucial role in air quality management and hazard mitigation. This study explores the accuracy of Artificial Intelligence (AI) models: adaptive-network-based fuzzy inference system (ANFIS) and multi-layered perceptron artificial neural network (mlp-NN) over the southwestern United States (SWUS) based on the observed dust data from IMPROVE stations. The ambient fine dust (PM2.5) and coarse dust (PM10) concentrations at monthly and seasonal timescale from 1990-2020 are modeled using average daily maximum wind speed (W), average precipitation (P), and average air temperature (T) available from North American Regional Reanalysis (NARR). The model’s performance is measured using correlation (r), root mean square error (RMSE), and percentage bias (% BISA). ANFIS model generally performs better than mlp-NN model in predicting regional dustiness over the SWUS region with r of 0.77 and 0.83 for monthly and seasonal fine dust respectively. AI models perform better in predicting regional dustiness at a seasonal timescale than the monthly timescale for both fine dust and coarse dust. AI models better predict fine dust than coarse dust at both monthly and seasonal timescales. Compared to precipitation, the near-surface average temperature is the more important predictor of the regional dustiness at both monthly and seasonal timescale. However, compared to the monthly timescale, air temperature is less more important predictor than precipitation at the seasonal timescale for PM2.5 and vice versa for PM10. The findings of this study demonstrate that the AI models have a good potential to predict monthly and seasonal fine and coarse dust at acceptable accuracy based on basic climatic data.

Near-surface humidity (Qa) is a key parameter that modulates oceanic evaporation and influences the global water cycle. Remote sensing observations act as feasible sources for long-term and large-scale Qa monitoring. However, existing satellite Qa retrieval models are subject to apparent uncertainties due to model errors and insufficient training data. Based on in situ observations collected over the China Seas over the last two decades, a deep learning approach named Ensemble Mean of Target deep neural networks (EMTnet) was proposed to improve the satellite Qa retrieval over the China Seas for the first time. The EMTnet model outperforms five representative existing models by nearly eliminating the mean bias and significantly reducing the root-mean-square error in satellite Qa retrieval. According to its target deep neural networks selection process, the EMTnet model can obtain more objective learning results when the observational data are divergent. The EMTnet model was subsequently applied to produce a 30-year monthly gridded Qa data over the China Seas. It indicates that the climbing rate of Qa over the China Seas under the background of global warming are probably underestimated by current products.

The aim were to determine the drinking water preferences of people applied to a family health center. This cross-sectional study was carried out from April 01st to May 31st, 2022. The data were evaluated using the chi-square test and percent ratios with a significant of P < 0.05. The mean age of all 432 respondents was 48.03±15.86. It was determined that those aged 31-45 had drunk more bottled water (p<0.01) and more spring water (p<0.001), that those aged 65 and over more purified water (p<0.001), that women more tap water (p<0.001), that married people had drunk more demijohn and tap water (p<0.001, each one), that divorced/widows had drunk more packaged and purified water (p<0.001, each one), that the illiterate/literate bottled water (p<0.001, per one), that those who had no income/people who lived on the state or municipal assistance only carboy water (p<0.001). This study suggests that the biggest factor that positively affected the drinking water preference was the packaging of the water. However, the drinking rates of mains water and spring water were quite low. Convincing the authorities to make the mains water drinkable is of great importance in terms of overcoming the public's distrust of mains water.

Impervious surface area (ISA) is a crucial indicator for quantitative urban studies. It is also important for land use land cover classification, groundwater recharge, sustainable development, urban heat island effects, and more. Spectral ISA mapping suffers from mixed pixel problems, especially with bare soil. This study aims to develop an ISA index for spatiotemporal urban mapping from common multispectral bands by reducing soil signature better than in previous studies. This study proposed a global impervious surface area index (GISAI) enhancing ISA mapping accuracy using a temporal parameter of the remote sensing (RS) dataset. Bare soil spectral reflectance shows more fluctuation than urban ISA. Therefore, the study uses minimum composites of earlier urban indices to compile minimum soil signature. It is later improved by removing water, increasing the contrast between bare soil and urban ISA and reducing bright bare soil area. This study maps the ISA of all 12 megacities using the annual RS image collection from 2021. GISAI reduced the bare soil signature and achieved an overall accuracy of 87.29%, F1-score of 0.84, and Kappa coefficient of 0.75. However, it has some limitations with grey bare soil and barren hilly areas. By limiting bare soil signature, GISAI broadens the scope of inter-urban studies globally and lengthens potential urban time-series analysis using common multispectral bands.

Monsoon winds drive upwelling along the eastern coast of India during the south-west (SW) monsoons. These winds also provide alongshore windstress (AWS) resulting in positive cross-shore Ekman transport (ET) from late May to the end of September. While instances of high ET and sea surface temperature (SST) based upwelling index (UI_SST) were observed along different parts of the coast, UI_SST was weaker in the northern section in the earlier part of monsoon. This was even in the presence of maximum AWS and ET during the 10 years analysis period spanning January-2009 to December-2018. Additionally, negative sea surface height anomalies (SSHAs), typically associated with coastal upwelling, were observed only along the southern-most coast. An empirical orthogonal function (EOF) analysis revealed two coherent modes of SSHA variation. The first principal component (PC) showed a SSHA signature coincident spatio-temporally with the first downwelling Kelvin wave, closely associated with the equatorial zonal winds that drive coastal Kelvin waves. The third PC with a coastal SSHA pattern similar to the second upwelling Kelvin wave was associated with offshore ET along the northern part of the Indian east coast. Time series of the two PCs exhibited suppression of coastal upwelling by downwelling Kelvin waves during May-July along the northeastern coast of India. Local AWS driven ET was the primary driver of coastal upwelling with the weakening of the remotely forced Kelvin waves in August. A coherent mode consisting of negative coastal SSHA signature was excited in response to local AWS driven ET during the upwelling period. This study examined the spatio-temporal variability of SW monsoon coastal upwelling along the east coast of India and illustrated the role of equatorial windstress forced first downwelling Kelvin wave in suppressing upwelling in the northern part of the coast during early SW monsoon season.

The three-dimensional structure of Gulf of Mexico’s warm-core rings, detaching from the Loop Current, is investigated using satellite altimetry and a large set of ARGO float profiles. Reconstruction of the Loop Current rings (LCRs) vertical structure from sea surface height observations is made possible by the use of the gravest empirical modes method (GEM). The GEMs are transfer functions that associate a value of temperature and salinity for each variable pair {dynamic height;pressure}, and are computed by estimating an empirical relationship between dynamic height and the vertical thermohaline structure of the ocean. Between 1993 and 2021, 40 LCRs were detected in the altimetry and their three-dimensional thermohaline structure was reconstructed, as well as a number of dynamically relevant variables (geostrophic and cyclogeostrophic velocity, relative vorticity, potential vorticity, available potential energy and kinetic energy density etc.). The structure of a typical LCR was computed by fitting an analytical stream function to the LCRs dynamic height signature and reconstructing its vertical structure with the GEM. The total heat and salt contents and energy of each LCR were computed and their cumulative effect on the Gulf of Mexico’s heat, salt and energy balance is discussed. We show that LCRs have a dramatic impact on these balances and estimate that residual surface heat fluxes of -13 Wm−2 are necessary to compensate their heat input, while the fresh water outflow of the Mississippi river approximately compensates for their salt excess input. An average energy dissipation of O[10−10−10−9] Wkg−1 would be necessary to balance their energy input.

Sun glint, i.e., direct solar radiation reflected from a water surface, negatively affects the accuracy of ocean color retrieval schemes if entering the field-of-view of the observing instrument. Herein, a simple and robust method to quantify the sun glint contribution to top-of-atmosphere (TOA) reflectances in the visible (VIS) and near-infrared (NIR) is proposed, exploiting concomitant observations of the sun glint’s morphology in the shortwave infrared (SWIR) characterized by reflectance contrasts typically higher than those resulting from other in-water or atmospheric processes. The proposed method, termed Glint Removal through Contrast Minimization (GRCM), requires high spatial resolution (ca. 10–50 m) imagery to resolve the sun glint’s characteristic morphology, meeting additional criteria on radiometric resolution and temporal delay between the individual band’s acquisitions. It has been applied with good success to a selection of Landsat 8 (L8) Operational Land Imager (OLI) scenes encompassing a wide range of environmental conditions in terms of observation geometry and glint intensity, as well as aerosol and Rayleigh optical depth. The method proposed herein is entirely image based and does not require ancillary information on the sea surface roughness or related parameters (e.g., surface wind), neither the presence of clear water areas in the image under consideration. Limitations of the proposed method are discussed, and its potential for sensors other than OLI and applications beyond glint removal is sketched.

Surface water availability is a fundamental environmental variable to implement effective climate adaptation and mitigation plans, as expressed by scientific, financial and political stakeholders. Recently published requirements urge the need for homogenised access to long historical records at a global scale, together with the standardised characterisation of the accuracy of observations. While satellite altimeters offer world coverage measurements, existing initiatives and online platforms provide derived water level data. However, these are sparse, particularly in complex topographies. This study introduces a new methodology in two steps 1) teroVIR, a virtual station extractor for a more comprehensive global and automatic monitoring of water bodies, and 2) teroWAT, a multi-mission, interoperable water level processor, for handling all terrain types. L2 and L1 altimetry products are used, with state-of-the-art retracker algorithms in the methodology. The work presents a benchmark between teroVIR and current platforms in West Africa, Kazakhastan and the Arctic: teroVIR shows an unprecedented increase from 55% to 99% in spatial coverage.A large-scale validation of teroWAT results in an average of unbiased root mean square error ubRMSE of 0.638 m on average for 36 locations in West Africa. Traditional metrics (ubRMSE, median, absolute deviation, Pearson coefficient) disclose significantly better values for teroWAT when compared with existing platforms, of the order of 8 cm and 5% improved respectively in error and correlation. teroWAT shows unprecedented excellent results in the Arctic, using a L1 products based algorithm instead of L2 one, reducing the error of almost 4 m on average. To further compare teroWAT with existing methods, a new scoring option, teroSCO, is presented, measuring the quality of the validation of time series transversally and objectively across different strategies. Finally, teroVIR and teroWAT are implemented as platform-agnostic modules and used by flood forecasting and river discharge methods as relevant examples. A review of various applications for miscellaneous end-users is given, tackling the educational challenge raised by the community.

Towards a better understanding of vegetation, permafrost, climate, landscape and lithology control on major and trace element (including macro and micro-nutrients and toxicants) transport in riverine systems, we studied two medium size (100-150 thousand km² watershed area) pristine rivers (Taz and Ket) of boreal and subarctic zone, western Siberia. Choosing the river basins of very low population density (< 1 people km-²) in the absence of any industrial or agricultural activity allowed testing the sole effect of natural factors and long-range atmospheric transfer on hydrochemistry of riverine solutes during the open water period. In the permafrost-bearing Taz River (main stem and 17 tributaries), sizable control of vegetation on element concentration was revealed. In particular, light coniferous and broadleaf mixed forest controlled DOC, and some nutrients (N, Mn, Fe, Mo, Cd, Ba); deciduous needleleaf forest positively correlated with macronutrients (P, Si, Mg, P, Ca) and Sr, and dark needle-leaf forest impacted Ntot, Al and Rb. Organic C stock in the upper 30-100 cm soil positively correlated with Be, Mn, Co, Mo, Cd, Sb, and Bi. The lithological control was generally poorly pronounced, due to abundant peat deposits overlaying the mineral strata. However, cretaceous carbonate mineral-bearing sedimentary deposits positively impacted the pH and concentration of Si, Mg, Ca and Cs. In the Ket River basin (large right tributary of the Ob River), we revealed the correlations between the phytomass stock at the watershed and alkaline-earth metals and U concentration in the river water. This control was weakly pronounced during high-water period (spring flood) and mostly evidenced during summer low water period.

The pressure on freshwater resources is leading to diminishing flows in some of the critical river systems across the globe and India is no exception and this is mainly because of water withdrawal for irrigation, which is often to the tune of 70% to 80% with some proportion for domestic and industrial use. While graduating from the concept of environmental flows and its assessment methodologies in India, the water-managers, the researchers and the conservationists are now moving towards answering the next question if the rivers are to be revived, then where will the water come from, especially in the case of over-allocated rivers, including the river Ganga. While the logical way is to look at the biggest user of water, i.e. irrigation, it remains to be seen whether the irrigation water savings will actually lead to enhancing flows in a river, complementing the efforts towards maintaining e-flows in rivers, or whether it will lead to more area under agriculture, bring changes in cropping patterns towards more water-intensive crops or result in something else. This is a growing debate across the globe, where India is no exception, and there has been a wide range of opinions in this regard. This paper discusses the process, findings and lessons from a joint initiative involving farmers, the Uttar Pradesh state Irrigation and Water Resources Department, Bijnor District Administration and a conservation organisation to enhance flows in a rivulet, called karula River, which is part of the Ganga river system.

Carbazole is one of the typical heterocyclic aromatic compounds (NSO-HETs) observed in pol-luted urban atmosphere, which has become a serious environmental concern. The most im-portant atmospheric loss process of carbazole is the reaction with OH radical. The present work investigated the mechanism of OH-initiated atmospheric oxidation degradation of carbazole by using density functional theory (DFT) calculations at the M06-2X/6-311++G(3df,2p)//M06-2X/6-311+G(d,p) level. The rate constants were determined by the Rice-Ramsperger-Kassel-Marcus (RRKM) theory. The lifetime of carbazole determined by OH was compared with other typical NSO-HETs. The theoretical results show that the degradation of carbazole initiated by OH radical includes four types of reactions: OH additions to “bend” C atoms, OH additions to “benzene ring” C atoms, H abstractions from C-H bonds and the H ab-straction from N-H bond. The OH addition to C1 atom and the H abstraction from N-H bond are energetically favorable. The main oxidation products are hydroxycarbazole, dialdehyde, carba-zolequinone, carbazole-ol, hydroxy-carbazole-one and hydroperoxyl-carbazole-one. The calcu-lated overall rate constant of carbazole oxidation by OH radical is 6.52 × 10−12 cm3 molecule−1 s−1 and the atmospheric lifetime is 43.92 h under the condition of 298 K and 1 atm. The lifetime of carbazole determined by OH radical is similar with that of dibenzothiophene oxidation but longer than those of pyrrole, indole, dibenzofuran and fluorene. This work provides a better understanding of the reactivity of carbazole in the atmospheric environment, the formation of secondary organic aerosols, and the chemical degradation and removal of carbazole in the at-mospheric environment.

Silicified ostracods from the Lower Carboniferous (Lower Tin Mountain Limestone; Kinderhookian-early Osagean; 350-358.9 Ma) of Lost Burro Gap, Death Valley region, Inyo County, California, USA augment the diversity of Paleozoic ostracods of western North America. Acid maceration of pelmatozoan, micritic and silicate clay-rich micritic marine limestones yielded the following palaeocopid and podocopid ostracods: Acratia spp., Bairdia quasilecta Bushmina, 1975, Bairdia sp. cf. B. orientalis, Ceratobairdia sp., Kirkbya panamintensis sp. nov., Rectobairdia sp. cf. R. legumen, and Silenites sp. This is the first report of Ceratobairdia and Silenites from the Tin Mountain Limestone. These ostracods occupied a Panthalassan carbonate ramp environment, and represent part of a fauna that was widespread in shallow marine waters of Panthalassa.

Among the various complex systems that we experience each day, there is the physical phenomenon of infiltration. Infiltration is considered as the dynamics of water flow in the subsurface soil that in this work is framed for the context of civil construction. This issue is approached with the use of mathematical models and stochastic techniques, however, there are hardships in collecting the samples in the field, the adoption of a scale type, the influence in soil layers interfaces, the effects of soil anisotropic characteristics and so on. In this study, methodology is proposed to deal with the soil sampling input such as the retro model, constitutive model and optimization algorithms. Additionally, an automatic calibration is set forth to fix parameters from the mathematical model submitted. Especially among the existing Optimization Algorithms there are Genetic Algorithms and the Generalized Simulated Annealing Algorithm (GSA). This work presents an overview of these optimization methods and a proposal for a more efficient and faster algorithm called GSA-HYBRID based on convergence gradient technique. The applied strategy depends on the type of case study considering its physical properties and constraints. In this sense, it is found that while Genetic Algorithms are able to replicate the optimization surface, Generalized Simulated Annealing is much more adequate in characterizing the system at an extremely low computational cost. Nevertheless, the hybrid technique GSA-HYBRID performed the fastest. Further research is necessary to implement the novel GSA-HYBRID algorithm due to its flexibility and higher speed, also, studying its application at different case studies.

The way Norway is spearheading electrification in the transport sector is of global interest, and details of its policies and emission impacts represent a reference. We used the NERVE model, a bottom-up high-resolution traffic emission model, to calculate all exhaust emissions in Norway (2009-2020). This allows us to evaluate the co-benefit of policies to target climate change mitigation and air quality. We have analyzed local municipal data with regards to traffic growth, road network influences, vehicle composition, emissions and energy consumption. Light vehicle CO2 emissions per kilometre have been reduced by 22% since 2009, mainly driven by an increaseing bio-fuel mixing and BEV share. BEVs are mostly located in and around the main cities, areas with young vehicle fleets, and strong local incentives. Beneficiaries of all BEVs incentives have been a subset of the population with strong economic indicators. The incentivized growth in the share of diesel-fuelled passenger vehicle has been turned, and together with Euro6 emission standards, light vehicle NOx emissions have been halved since peaking in 2014. BEV represent an investment in emission reductions in years to come and current sales set up for an accelerated decline in emissions despite growth in traffic.

This study introduces an alternative to the existing methods for measuring ocean currents based on a recently developed technology. The SailBuoy is an unmanned surface vehicle powered by wind and solar panels that can navigate autonomously to predefined way-points and record velocity profiles using an integrated downward-looking Acoustic Doppler Profiler (ADCP). Data, collected on two validation campaigns, shows a satisfactory correlation between the SailBuoy current records and traditional observation techniques such as bottom mounted and moored current profilers and moored single-point current meter. While the highest correlations were found in tidal signals, strong current, and calm weather conditions, low current speeds and varying high wave and wind conditions reduced correlation considerably. Filtering out some events with high sea surface roughness associated with high wind and wave conditions may increase the SailBuoy ADCP listening quality and lead to better correlations. Not yet resolved is a systematic offset between the measurements obtained by the SailBuoy and the reference instruments of ± 0.03 m/s. Possible reasons are discussed to be the differences between instruments (various products) as well as changes in background noise levels due to environmental conditions.