Seasonal Averages Research Articles

Evaluating the reproducibility and paleo-hydroclimate potential of coral skeletal Ba/Ca in the Gulf of Chiriquí, Panama

Coral skeletal Ba/Ca (Ba/Cacor) has been found to be highly correlated with river discharge in some coastal settings. However, any hydrologic interpretation of Ba/Cacor time-series requires thorough evaluation of regional climate, even at sites in close proximity to one another. Here we explore how two corals can be used to provide insight into hydroclimate in the Gulf of Chiriquí, Panama (GoC) using Ba/Cacor, a region whose climate is dictated by seasonal and lower frequency shifts in the Intertropical Convergence Zone. The main purpose of this study is to provide a preliminary assessment of Ba/Cacor replication in pursuit of a regional geochemical network. The two corals analyzed in this study (Secas Island, S1 and Coiba, IC4A-2) were collected ∼74 km apart and grew in different reef settings at vastly different distances from the main river discharge points. Analytical uncertainty prevents complete confidence determining whether a small geochemical offset Ba/Cacor exists between our two records. However, temporal variability in S1 and IC4A-2 are well correlated at all examined temporal scales (rmonthly = 0.70, rannual = 0.65, rwet season = 0.67, rdry season = 0.55, rannual amplitude = 0.77). Considering a potential geochemical offset may exist between the two corals, we took a conservative approach and examined S1 and IC4A-2 separately to establish a relationship with river discharge. Both corals, barring IC4A-2 dry season averages, are statistically significantly correlated to river discharge, permitting the creation of Reduced Major Axis regressions to quantify the relationship. Ultimately, in regions where river discharge dominates, a network of Ba/Cacor records may assist in reconstructing regional variability or notable deviations in hydroclimate. With additional and temporally longer Ba/Cacor records, we will be able to apply our framework to more clearly reconstruct river discharge variability throughout the GoC.

Response of PM2.5 and O3 to Emission Reductions in Nanjing Based on Random Forest Algorithm

High levels of fine particulate matter (PM2.5) and ozone (O3) in ambient air affect climate change and also endanger human health and ecosystems. Air pollution in Nanjing has been improving since the implementation of the "Air Pollution Prevention and Control Action Plan" in 2013. However, Nanjing still faces PM2.5 and O3 pollution. Evaluating the response of pollutant concentrations to the reductions in precursor emissions is helpful to obtain effective strategies of emission reduction to improve pollution levels. The sensitive simulations of emission perturbation in atmospheric chemistry models directly demonstrate the response of pollution to the reductions in emissions. Nevertheless, these sensitive simulations are limited in computing time and resources. The random forest algorithm was trained by using the simulation results of the atmospheric chemical transport model (GEOS-Chem) in 2015. The changes in daily PM2.5 and daily maximum eight-hour O3 (MDA8 O3) concentrations in Nanjing in 2019 were efficiently predicted under different reduction scenarios of anthropogenic emissions. The simulations showed that the seasonal average of ρ(PM2.5) in Nanjing would decrease by 2-4 μg·m-3 with the reduction in anthropogenic emissions of 10% in 2019 in China. In the case of controlling only local emissions in Nanjing, the concentrations of PM2.5 in Nanjing decreased significantly without local anthropogenic emissions. Additionally, the simulations showed that the annual average of ρ(PM2.5) in Nanjing could be lower than the national secondary limit (35 μg·m-3) when the anthropogenic emission reduction in China was higher than 20% in 2019. For ozone, the equal proportional emission reductions in nitrogen oxides (NOx) and volatile organic pollutants (VOCs) of O3 precursors in China likely led to the increase in seasonal average concentrations of O3 in Nanjing. For the proportional reduction of anthropogenic emissions by 10%-50% in China, the seasonal average of ρ(MDA8 O3) in Nanjing in 2019 would increase by 1-3 μg·m-3 in spring, 1-4 μg·m-3 in autumn, and 3-11 μg·m-3 in winter, respectively, compared with that in the base simulation. With the reduction in anthropogenic NOx emission by 10% and VOCs by 20%, the seasonal average of ρ(MDA8 O3) in Nanjing would decrease by 3-6 μg·m-3. On this basis, further increasing the proportion (30%) of VOCs emission reduction could reduce the annual average of ρ(MDA8 O3) in Nanjing by 7 μg·m-3. However, the annual average of ρ(MDA8 O3) of Nanjing in 2019 increased by 1 μg·m-3, with the local emission reduction of NOx by 10% and VOCs by 30%. Therefore, this showed that the key to alleviate ozone pollution in Nanjing is a reasonable control ratio of ozone precursor emissions and the implementation of regional joint prevention and control. In order to effectively reduce the O3 pollution in Nanjing, the emission reduction ratio of NOx and VOCs in China should be less than 1:2. The response of pollutant concentrations to reductions in precursor emissions were efficiently obtained by the random forest algorithm and GEOS-Chem model. The simulations would provide the scientific basis for the emission control strategy to alleviate air pollution.

Accuracy of Estimated Crop Evapotranspiration Using Locally Developed Crop Coefficients against Satellite-Derived Crop Evapotranspiration in a Semiarid Climate

Actual crop evapotranspiration (ETa) is measured or estimated using different methods, and its accuracy is critical for water management under precision agriculture. The objective of this study was to compare maize ETa estimated by the two-step approach using a locally developed crop coefficient curve with satellite-retrieved evapotranspiration by six models incorporated in the OpenET to identify the best evapotranspiration estimation alternatives to the two-step approach for water management in northern New Mexico. Maize (Zea mays L.) was planted at the NMSU Agricultural Science Center at Farmington from 2017 to 2022 and uniformly managed across years. Water management in plants was based on maize’s actual evapotranspiration estimated as the product of the reference evapotranspiration and the local crop coefficient, which is described as a third-order polynomial function of the accumulated heat units by maize plants. For the same growing seasons, maize ETa was retrieved from satellite, and was estimated by six models listed within the OpenET from 2017 to 2022. The results show that maize daily ETa was consistently smaller when measured by SIMS and PT-JPL during maize initial and actively growing stages, while ETc(kc), SIMS and eeMETRIC showed similar maize daily ETa during maize full canopy development and mid-season, and which overcome the evapotranspiration estimated by DisALEXI, PT-JPL, geeSEBAL, and SSBop. ETc(kc) drastically dropped and became the lowest value among all ETa estimation models after the first fall snow or the first killing frost. Regarding the seasonal average, all six models included in OpenET showed smaller maize evapotranspiration. Maize seasonal evapotranspiration varied from 589.7 to 683.2 mm. eeMETRIC compares most similarly to the ETc(kc) model, followed by SIMS, with percent errors of 2.58 and 7.74% on a daily basis and 2.43 and 7.88% on a seasonal basis, with the lowest MBE and RMSE values, respectively, and could be used as an alternative for maize actual daily evapotranspiration for water management in northern New Mexico. The results of this study could be used by water managers and crop growers to improve water management in the Four Corners region, using eeMETRIC for crop water use to improve water management and conservation under sustainable agriculture.

Exploratory study of the Sentinel-3 level 2 product for monitoring chlorophyll-a and assessing ecological status in Danish seas

In situ Chl-a data were used to perform empirical calibration and validation of Sentinel-3 level 2 product in Danish marine waters. Comparing in situ data with both same-day and ±5 days moving averaged Sentiel-3 Chl-a values yielded two similar positive correlations (p > 0.05) with rpearson values of 0.56 and 0.53, respectively. However, as the moving averaged values resulted in significantly more available data than daily matchups (N = 392 vs. N = 1292) at a similar quality of correlation with similar model parameters (slope (1.53 and 1.7) and intercept (−0.28 and −0.33) respectively), which were not significantly different (p > 0.05), the further analyses were focused on ±5 days moving averaged values. A thorough comparison of seasonal and growing season averages (GSA) also showed a very good agreement, except for a few stations characterized by very shallow depth. Overestimation by the Sentinel-3 occurred in shallow coastal areas and was attributed to the interferences from benthic vegetation and high levels of Colored Dissolved Organic matter (CDOM) interfering with the Chl-a signals. Underestimation observed in the inner estuaries with shallow Chl-a rich waters, however, seen as a result of self-shading at high Chl-a concentrations, reducing effective absorption by phytoplankton. Besides the observed minor disagreements, there was no significant difference when the GSA values from in situ and Sentinel-3 were compared for all three water types (p > 0.05, N = 110). Analyzing Chl-a estimates along a depth gradient showed significant (p < 0.001) non-linear trends of declining concentrations from shallow to deeper waters for both in situ (explaining 15.2 % of the variance (N = 109)) and Sentinel-3 data (explaining 36.3 % of the variance (N = 110)), with higher variability in shallow waters. Furthermore, Sentinel-3 enabled full spatial coverage of all 102 monitored water bodies providing GSA data at much higher spatial and temporal resolutions for good ecological status (GES) assessment compared to only 61 through in situ sampling. This underlines the potential of Sentinel-3 for substantially extending the geographical coverage of monitoring and assessment. However, the systematic over- and underestimation of Chl-a in shallow nutrient rich inner estuaries through Sentinel-3 requires further attention to enable routine application of the Sentinel-3 level 2 standard product in the operational Chl-a monitoring in Danish coastal waters. We provide methodological recommendations on how to improve the Sentinel-3 products' representation of in situ Chl-a conditions. Continued frequent in situ sampling remains important for monitoring as these measurements provide essential data for empirical calibration and validation of satellite based estimates to reduce possible systematic bias.

The Prediction of The Drought Index in The Indragiri Watershed Using SARIMA and SPI Methods

Farmers are concerned about the problem of drought in agriculture because it will disrupt the production system and cause losses. In 2015, the Indragiri Hulu area experienced a drought on 273 hectares of agricultural land. This area is part of the Indragiri River Watershed in Riau Province. To prevent the impact of future droughts, forecasting is required. The level of drought is influenced by rainfall, so the Seasonal Auto Regressive Integrated Moving Average (SARIMA) method is used to model rainfall data. In addition, the Standardized Precipitation Index (SPI) is also used to analyze the drought index. After the analysis, it was found that the best model in forecasting is SARIMA model (1,0,1)(2,1,1)12 with an average residual square of 18.0175, and the drought index in the Indragiri watershed is currently included in the "Normal" category. The results of this forecasting are expected to assist the Riau Province National Disaster Management Agency in anticipating and mitigating the impact of future drought disasters.

Multi-decadal observations in the Alps reveal less and wetter snow, with increasing variability

Snowpack is an important temporal water storage for downstream areas, a potential source of natural hazards (avalanches or floods) and a prerequisite for winter tourism. Here, we use thousands of manual measurements of the water equivalent of the snow cover (SWE) from almost 30 stations between 1,200 and 2,900 m a.s.l. from four long-term monitoring programs (earliest start in 1937) in the center of the European Alps to derive daily SWE based on snow depth data for each station. The inferred long-term daily SWE time series were analyzed regarding spatial differences, as well as potential temporal changes in variability and seasonal averages during the last 7 decades (1957–2022). The investigation based on important hydro-climatological SWE indicators demonstrates significant decreasing trends for mean SWE (Nov-Apr) and for maximum SWE, as well as a significantly earlier occurrence of the maximum SWE and earlier disappearance of the continuous snow cover. The anomalies of mean SWE revealed that the series of low-snow winters since the 1990s is unprecedented since the beginning of measurements. Increased melting during the accumulation period below 2000 m a.s.l is also observed–especially in the most recent years–as well as slower melt rates in spring, and higher day-to-day variability. For these trends no regional differences were found despite the climatological variability of the investigated stations. This indicates that the results are transferable to other regions of the Alps.

Identify the key emission sources for mitigating ozone pollution: A case study of urban area in the Yangtze River Delta region, China

Ozone (O3) has become the most critical air pollutant in the Yangtze River Delta (YRD) region of China. Research on the O3 formation mechanism and its precursor sources (including nitrogen oxides (NOX) and volatile organic compounds (VOCs)) could provide a theoretical basis for mitigating O3 pollution in this region. In this study, simultaneous field experiments were conducted for air pollutants in a typical urban area (Suzhou) in the YRD region in 2022. The capacity of in-situ O3 formation, O3-NOX-VOCs sensitivities and sources of O3 precursors were analyzed. The results showed that in-situ formation contributed 20.8 % of the O3 concentration in the warm season (April to October) of the Suzhou urban area. Compared with the warm season average, the concentrations of various O3 precursors increased on pollution days. The O3-NOX-VOCs sensitivity was the VOCs-limited regime based on the average concentrations during the warm season. O3 formation was most sensitive to anthropogenic VOCs, of which oxygenated VOCs, alkenes and aromatics were the key species. There was a VOCs-limited regime in spring and autumn, while a transitional regime in summer due to the changes in NOX concentrations. This study considered NOX emission from VOCs sources and calculated the contribution of various sources to O3 formation. The results of VOCs source apportionment showed that diesel engine exhaust and fossil fuel combustion had a dominant proportion, but O3 formation presented significant negative sensitivities to the above two sources because of their high NOX emissions. There were significant sensitivities of O3 formation to gasoline vehicle exhaust and VOCs evaporative emissions (gasoline evaporation and solvent usage). The contribution of VOCs evaporative emissions during the O3 pollution episode was significantly higher than the average; therefore, controlling VOCs evaporative emissions during the O3 pollution episode is critical. These results provide feasible strategies to mitigate O3 pollution.

Fire weather index data under historical and shared socioeconomic pathway projections in the 6th phase of the Coupled Model Intercomparison Project from 1850 to 2100

Abstract. Human-induced climate change is increasing the incidence of fire events and associated impacts on livelihood, biodiversity, and nature across the world. Understanding current and projected fire activity together with its impacts on ecosystems is crucial for evaluating future risks and taking actions to prevent such devastating events. Here we focus on fire weather as a key driver of fire activity. Fire weather products that have a global homogenous distribution in time and space provide many advantages to advance fire science and evaluate future risks. Therefore, in this study we calculate and provide for the first time the Canadian Fire Weather Index (FWI) with all available simulations of the 6th phase of the Coupled Model Intercomparison Project (CMIP6). Furthermore, we expand its regional applicability by combining improvements to the original algorithm for the FWI from several packages. A sensitivity analysis of the default version versus our improved version shows significant differences in the final FWI. With the improved version, we calculate the FWI using average relative humidity in one case and minimum relative humidity in another case. We provide the data for both cases while recommending the one with minimum relative humidity for studies focused on actual FWI values and the one with average relative humidity for studies requiring larger ensembles. The following four annual indicators, (i) maximum value of the FWI (fwixx), (ii) number of days with extreme fire weather (fwixd), (iii) length of the fire season (fwils), and (iv) seasonal average of the FWI (fwisa), are made available and are illustrated here. We find that, at a global warming level of 3 ∘C, the mean fire weather would increase on average by at least 66 % in duration and frequency, while associated 1-in-10-year events would approximately triple in duration and increase by at least 31 % in intensity. Ultimately, this new fire weather dataset provides a large ensemble of simulations to understand the potential impacts of climate change spanning a range of shared socioeconomic narratives with their radiative forcing trajectories over 1850–2100 at annual and 2.5∘ × 2.5∘ resolutions. The produced full global dataset is a freely available resource at https://doi.org/10.3929/ethz-b-000583391 (Quilcaille and Batibeniz, 2022) for fire danger studies and beyond, which highlights the need to reduce greenhouse gas emissions for reducing fire impacts.

Response of the sea surface temperature to heatwaves during the France 2022 meteorological summer

Abstract. The summer of 2022 was memorable and record-breaking, ranking as the second hottest summer in France since 1900, with a seasonal surface air temperature average of 22.7 ∘C. In particular, France experienced multiple record-breaking heatwaves during the meteorological summer. As the main heat reservoir of the Earth system, the oceans are at the forefront of events of this magnitude which enhance oceanic disturbances such as marine heatwaves (MHWs). In this study, we investigate the sea surface temperature (SST) of French maritime basins using remotely sensed measurements to track the response of surface waters to the atmospheric heatwaves and determine the intensity of such feedback. Beyond the direct relationship between SSTs and surface air temperatures, we explore the leading atmospheric parameters affecting the upper-layer ocean heat budget. Despite some gaps in data availability, the SSTs measured during the meteorological summer of 2022 were record-breaking, the mean SST was between 1.3 and 2.6 ∘C above the long-term average (1982–2011), and the studied areas experienced between 4 and 22 d where the basin-averaged SSTs exceeded the maximum recorded basin-averaged SSTs from 1982 to 2011. We found a significant SST response during heatwave periods with maximum temperatures measured locally at 30.8 ∘C in the north-western Mediterranean Sea. Our results show that in August 2022 (31 July to 13 August), France experienced above-average surface solar radiation correlated with below-average total cloud cover and negative wind speed anomalies. Our attribution analysis based on a simplified mixed-layer heat budget highlights the critical role of ocean–atmosphere fluxes in initiating abnormally warm SSTs, while ocean mixing plays a crucial role in the cessation of such periods. We find that the 2 m temperatures and specific humidity that are consistently linked to the advection of warm and moist air masses are key variables across all the studied regions. Our results reveal that the influence of wind on heatwaves is variable and of secondary importance. Moreover, we observe that the incident solar radiation has a significant effect only on the Bay of Biscay (BB) and the English Channel (EC) areas. Our study findings are consistent with previous research and demonstrate the vulnerability of the Mediterranean Sea to the increasing frequency of extreme weather events resulting from climate change. Furthermore, our investigation reveals that the recurring heatwave episodes during the summer of 2022 had an undeniable impact on all the surveyed maritime areas in France. Our study therefore provides valuable insights into the complex mechanisms underlying the ocean–atmosphere interaction and demonstrates the need for an efficient and sustainable operational system combining polar-orbiting and geostationary satellites to monitor the alterations that threaten the oceans in the context of climate change.

Clean air in Europe for all: A call for more ambitious action.

Clean air in Europe for all: A call for more ambitious action.

Estimation of the influenza-associated respiratory hospitalization burden using sentinel surveillance data, Lebanon, 2015-2020.

Influenza epidemics cause around 3 to 5 million cases of severe illness worldwide every year. Estimates are needed for a better understanding of the burden of disease especially in low- and middle-income countries. The objective of this study is to estimate the number and rate of influenza-associated respiratory hospitalizations in Lebanon during five influenza seasons (2015-2016 to 2019-2020) by age and province of residence in addition to estimating the influenza burden by level of severity. The severe acute respiratory infection sentinel surveillance system was used to compute influenza positivity from the influenza laboratory confirmed cases. The total of respiratory hospitalizations under the influenza and pneumonia diagnosis was retrieved from the Ministry of Public Health hospital billing database. Age-specific and province-specific frequencies and rates were estimated for each season. Rates per 100 000 population were calculated with 95% confidence levels. The estimated seasonal average of influenza-associated hospital admission was 2866 for a rate of 48.1 (95% CI: 46.4-49.9) per 100 000. As for the distribution by age group, the highest rates were seen in the two age groups ≥65 years and 0-4years whereas the lowest rate was for the age group 15-49 years. For the distribution by province of residence, the highest influenza-associated hospitalization rates were reported from the Bekaa-Baalback/Hermel provinces. This study shows the substantial burden of influenza in Lebanon mainly on high-risk groups (≥65 years and <5years). It is crucial to translate these findings into policies and practices to reduce the burden and estimate the illness-related expenditure and indirect costs.

Surveillance of Influenza and Other Airborne Transmission Viruses during the 2021/2022 Season in Hospitalized Subjects in Tuscany, Italy.

Winter in the northern hemisphere is characterized by the circulation of influenza viruses, which cause seasonal epidemics, generally from October to April. Each influenza season has its own pattern, which differs from one year to the next in terms of the first influenza case notification, the period of highest incidence, and the predominant influenza virus subtypes. After the total absence of influenza viruses in the 2020/2021 season, cases of influenza were again recorded in the 2021/2022 season, although they remained below the seasonal average. Moreover, the co-circulation of the influenza virus and the SARS-CoV-2 pandemic virus was also reported. In the context of the DRIVE study, oropharyngeal swabs were collected from 129 Tuscan adults hospitalized for severe acute respiratory infection (SARI) and analyzed by means of real-time polymerase chain reaction (RT-PCR) for SARS-CoV-2 and 21 different airborne pathogens, including influenza viruses. In total, 55 subjects tested positive for COVID-19, 9 tested positive for influenza, and 3 tested positive for both SARS-CoV-2 and the A/H3N2 influenza virus. The co-circulation of different viruses in the population requires strengthened surveillance that is no longer restricted to the winter months. Indeed, constant, year-long monitoring of the trends of these viruses is needed, especially in at-risk groups and elderly people.

Comparing the cooling effectiveness of operationalisable urban surface combination scenarios for summer heat mitigation

Extreme summer heat in cities exacerbates the vulnerability of urban communities to heatwaves. Vegetative and reflective urban surfaces can help reduce urban heat. This study investigated the impacts of urban trees, green roofs and cool roofs on heat mitigation during average and extreme summer conditions in temperate oceanic Melbourne, Australia. We simulated the city climate using ‘The Air Pollution Model’ (TAPM) at a 1 km spatial resolution over 10 years, which according to our review of the literature, was the most prolonged period for simulation in Melbourne. During a widespread heatwave event, some of the tested scenarios with combined surface parameters could reduce the extreme values of the energy budget components- sensible heat, latent heat, and storage heat fluxes up to seasonal averages compared to the existing situation for Melbourne (control). The scenario with the highest (reasonable maximum) ground-level vegetation, green roofs, and cool roofs could reduce air temperatures up to 2.4 °C. The simulations suggest that a combined strategy with vegetative and high-albedo surfaces will deliver higher effectiveness with maximum cooling benefits and cost-effectiveness than individual strategies in cities. These results suggest the importance of collaborative strategic planning of urban surfaces to make cities healthier, sustainable, and liveable.

Anomalous thermal and winds condition drove low surface chlorophyll-a in La Paz Bay during the 2015–2016 El Niño event

La Paz Bay (LPB) considered one of the most highly productive bays in the southwestern Gulf of California, Mexico, recently established as Priority Marine Region, supports many species of ecological importance. In this context, daily composites of surface chlorophyll a (Chl-aSAT), sea surface temperature (SST) satellite images derived from the MODIS-Aqua sensor, and a wind intensity index (WI) calculated with data recorded simultaneously at four points along the coast, were used to describe the strong 2015–2016 El Niño impact over the surface chlorophyll pigments in LPB, relative to its average for 2003–2013. Significant relationships between SST, WI (used as environmental factors) and Chl-aSAT (RSST = −0.74, RWI = −0.40, P < 0.01) were found using exponential models for the entire study period, with moderate correlation during El Niño winter condition (December 2015 to March 2016) between SST and Chl-aSAT (RSST = 0.45, P < 0.02). The Chl-aSAT showed unexpectedly low values (∼0.5–1.2 mg m−3) with one month-lag of the winter bloom period jointly with the presence of warmer-than-usual water (∼1.0 °C) and episodes of strong northwesterly winds (speed 8.0–10.0 m s−1) that began in autumn that drove a ∼2 month-lag in its winter peak. The Chl-aSAT and SST winter spatial patterns, showed low Chl-aSAT values (∼−0.5 to −1.0 mg m−3) along the bay and higher-than-usual SST by ∼ 0.5–1.0 °C relative to a year under the La Niña winter condition (2017–2018). This triggered the possible thermocline deepening and the lowest levels of nutrient supply to the photic layer during the El Niño winter episode. Hovmöller diagrams of Chl-aSAT and SST anomalies along the coast revealed a sharp drop to negative anomaly values (∼− 0.7 to −1.2 mg m−3), in-phase but negatively correlated with the SST coastal anomalies (R = − 0.88, P < 0.01). This biophysical coupling played a crucial role in the exceptional drop to the lowest winter Chl-aSAT levels seen in LPB since the MODIS satellite sensor began taking measurements in 2002 (Chl-aSAT values were lower than usual by ∼ 0.9–1.2 mg m −3). Besides the lowest winter levels observed, this research reflected the El Niño signal-driven changes over the basic seasonal average of the LPB climate system The non-significant negative trend observed in the winter average Chl-aSAT values, indicates that LPB may be less resilient to future environmental disturbances.

Assessment of SWAN and WAVEWATCH-III models regarding the directional wave spectra estimates based on Eastern Black Sea measurements

The spectral wave models are often calibrated or validated based on bulk wave data derived from spectral information such as the significant wave height (Hs) and mean period (Tm). However, the precision in the hindcasted spectral data was not evaluated in many seas, such as the Black Sea, due to the lack of spectral wave measurements. As part of this study, a spectral wave measuring buoy was installed near the cape Small Utrish to collect the spectral measurement data. The study objective is to assess the SWAN and WAVEWATCH-III (WWIII) models regarding the directional wave spectra estimates based on measurements conducted in the eastern Black Sea. For this purpose, the estimated spectral density distribution and spectral mean direction in terms of spectral frequency bins are assessed for both SWAN and WWIII outputs. The biases in variance densities are firstly calculated for both models at each frequency based on 14 710 spectral measurements, as well as the biases in the annual and seasonal averages. In addition, the correlation coefficients and root mean squared errors at each frequency are calculated and discussed. The accuracies in the mean direction at each spectral shape over the whole measurement period are secondly evaluated. Thirdly, the annual and seasonal averaged two-dimensional wave spectrums are discussed compared to measurements. The performances of both models are lastly verified based on bulk data measured at 8 locations on different coasts of the Black Sea. The results show that SWAN output fits better the average variance density shape at all frequency ranges f > 0.08Hz. However, it slightly overestimates the variance density in the 0.13–0.17 Hz range. In contrast to the SWAN model, WWIII data show only better correspondence in the 0.13–0.17 Hz frequency range but underestimate the lower frequency energies and overestimate higher frequency ranges. The SWAN model is therefore recommended for the spectral density estimate at seasonal and annual scales.

The benefits of homogenising snow depth series – Impacts on decadal trends and extremes for Switzerland

Abstract. Our current knowledge of spatial and temporal snow depth trends is based almost exclusively on time series of non-homogenised observational data. However, like other long-term series from observations, they are prone to inhomogeneities that can influence and even change trends if not taken into account. In order to assess the relevance of homogenisation for time-series analysis of daily snow depths, we investigated the effects of adjusting inhomogeneities in the extensive network of Swiss snow depth observations for trends and changes in extreme values of commonly used snow indices, such as snow days, seasonal averages or maximum snow depths in the period 1961–2021. Three homogenisation methods were compared for this task: Climatol and HOMER, which apply median-based adjustments, and the quantile-based interpQM. All three were run using the same input data with identical break points. We found that they agree well on trends of seasonal average snow depth, while differences are detectable for seasonal maxima and the corresponding extreme values. Differences between homogenised and non-homogenised series result mainly from the approach for generating reference series. The comparison of homogenised and original values for the 50-year return level of seasonal maximum snow depth showed that the quantile-based method had the smallest number of stations outside the 95 % confidence interval. Using a multiple-criteria approach, e.g. thresholds for series correlation (&gt;0.7) as well as for vertical (&lt;300 m) and horizontal (&lt;100 km) distances, proved to be better suited than using correlation or distances alone. Overall, the homogenisation of snow depth series changed all positive trends for derived series of snow days to either no trend or negative trends and amplifying the negative mean trend, especially for stations &gt;1500 m. The number of stations with a significant negative trend increased between 7 % and 21 % depending on the method, with the strongest changes occurring at high snow depths. The reduction in the 95 % confidence intervals of the absolute maximum snow depth of each station indicates a decrease in variation and an increase in confidence in the results.

Differential impact of the COVID-19 pandemic on primary care utilization related to common mental disorders in four European countries: A retrospective observational study.

The COVID-19 pandemic is commonly believed to have increased common mental disorders (CMD, i.e., depression and anxiety), either directly due to COVID-19 contractions (death of near ones or residual conditions), or indirectly by increasing stress, economic uncertainty, and disruptions in daily life resulting from containment measure. Whereas studies reporting on initial changes in self-reported data frequently have reported increases in CMD, pandemic related changes in CMD related to primary care utilization are less well known. Analyzing time series of routinely and continuously sampled primary healthcare data from Sweden, Norway, Netherlands, and Latvia, we aimed to characterize the impact of the pandemic on CMD recorded prevalence in primary care. Furthermore, by relating these changes to country specific time-trajectories of two classes of containment measures, we evaluated the differential impact of containment strategies on CMD rates. Specifically, we wanted to test whether school restrictions would preferentially affect age groups corresponding to those of school children or their parents. For the four investigated countries, we collected time-series of monthly counts of unique CMD patients in primary healthcare from the year 2015 (or 2017) until 2021. Using pre-pandemic timepoints to train seasonal Auto Regressive Integrated Moving Average (ARIMA) models, we predicted healthcare utilization during the pandemic. Discrepancies between observed and expected time series were quantified to infer pandemic related changes. To evaluate the effects of COVID-19 measures on CMD related primary care utilization, the predicted time series were related to country specific time series of levels of social distancing and school restrictions. In all countries except Latvia there was an initial (April 2020) decrease in CMD care prevalence, where largest drops were found in Sweden (Prevalence Ratio, PR = 0.85; 95% CI 0.81-0.90), followed by Netherlands (0.86; 95% CI 0.76-1.02) and Norway (0.90; 95% CI 0.83-0.98). Latvia on the other hand experienced increased rates (1.25; 95% CI 1.08-1.49). Whereas PRs in Norway and Netherlands normalized during the latter half of 2020, PRs stayed low in Sweden and elevated in Latvia. The overall changes in PR during the pandemic year 2020 was significantly changed only for Sweden (0.91; 95% CI 0.90-0.93) and Latvia (1.20; 95% CI 1.14-1.26). Overall, the relationship between containment measures and CMD care prevalence were weak and non-significant. In particular, we could not observe any relationship of school restriction to CMD care prevalence for the age groups best corresponding to school children or their parents. Common mental disorders prevalence in primary care decreased during the initial phase of the COVID-19 pandemic in all countries except from Latvia, but normalized in Norway and Netherlands by the latter half of 2020. The onset of the pandemic and the containment strategies were highly correlated within each country, limiting strong conclusions on whether restriction policy had any effects on mental health. Specifically, we found no evidence of associations between school restrictions and CMD care prevalence. Overall, current results lend no support to the common belief that the pandemic severely impacted the mental health of the general population as indicated by healthcare utilization, apart from in Latvia. However, since healthcare utilization is affected by multiple factors in addition to actual need, future studies should combine complementary types of data to better understand the mental health impacts of the pandemic.

Weekly External Load Performance Effects on Sports Injuries of Male Professional Football Players.

One of the most challenging issues professional football players face throughout their careers is injuries. Those injuries often result from suboptimal training programs that were not designed according to the players' individual needs. This prospective study aimed to examine in detail the effects of sports injuries on professional football players' weekly external load performances. Thirty-three male professional football players were monitored using 10-Hz Global Positioning System (GPS) units (Apex pro series, StatSports) during an entire season. The variables considered in the analysis were total distance (TD), high-speed running (HSR), accelerations (ACC), and decelerations (DEC). The comparisons were made between the four-week block before injury (-4T), four-week block after return (+4T), and players' season averages (S). Players displayed significantly higher values of TD, HSR, ACC, and DEC in the -4T, compared to the other two moments (+4T and S). Furthermore, the comparison between the +4T and S showed no significant variations in the GPS metrics. It was shown that a significant increase in players' weekly external load performance over a four-week period may have a negative effect on the occurrence of injuries from a professional football standpoint. Future research should consider the effects of injury severity on players' external load variations.

Ecogeomorphological condition and hydrological indicators of the self-purification capacity of rivers: A case study of Siminehrood River in Northwestern Iran

Aims: The steady development of human communities and the spread of industrial activities are major contributors to environmental pollution, especially the contamination of water resources. Population growth and thus the acceleration of municipal, industrial, and agricultural wastewater release have adversely affected these inestimable resources and restricted their accessibility. This work attempts to identify the ecogeomorphological condition of rivers in drainage basins emphasizing the Siminehrood River in Northwestern Iran. The purpose is to study all nonpoint source (PS) and PS pollutions and circumstances that weaken and intensify the pollution rate and self-purification capacity of rivers, especially in the Siminehrood River. Materials and Methods: All data and statistics were collected and their seasonal average was calculated. Maps and variables associated with the physical properties of drainage basins were then extracted through ArcGIS. The Schuler diagram was plotted through Chemistry software for all stations and each season to assess the type and chemical quality of the river's drinking water. HEC-RAS model, HEC-GEORAS extension, and ArcGIS were employed for simulation of river flow and calculation and determination of water surface profiles and other hydraulic characteristics of flow including water depth, water flow rate, stream shear stress, and stream power. Results: According to the analyses and results, the improper ecogeomorphological condition of rivers and their low self-purification capacity are directly correlated with the mean river water depth, water flow rate, slope of the river basin, and environmental differences. Conclusions: Within the study area, the highest environmental instability and the least self-purification capacity were observed downstream of the sub-basin in which the mean and maximum depth of water were, respectively, 3.10 m and 8.803 m. Insignificant water flow rate (0.86 m/s on average) and slope of <4% in the area have stagnated water flow in most areas and consequently declined the content of dissolved oxygen and the quality of water. Conclusively, this sub-basin can be reported as a region with an improper ecogeomorphological condition.

The Influence of Climate Variability on the Watermelon Production in Zanzibar

Climate change and variability, has embarked societies in Zanzibar to rely on horticulture (i.e. watermelon production) as an adaptive measure due to an unpromising situation of commonly used agricultural yields. Currently, there is either no or scant information that describes the influence of climate changes and variability to watermelon production in Zanzibar. Thus, this study aimed to determine the influence of climate variability on the quantity of watermelon production in Zanzibar. The study used both primary and secondary datasets, which include the anecdotal information collected from interviewers’ responses from four districts of Unguja and Pemba, and climate parameters (rainfall, maximum and minimum temperature (Tmax and Tmin) acquired from Tanzania Meteorological Authority (TMA) at Zanzibar offices. Pearson correlation was used for analyzing the association between watermelon production and climate parameters, while paired t-test was applied to show the significance of the mean differences of watermelon and climate parameters for two periods of 2014-2017 and 2018-2021, respectively. Percentage changes were used to feature the extent to which the two investigated parameters affect each other. The anecdotal responses were sorted, calculated in monthly and seasonal averages, plotted and then analyzed. Results have shown a strong correlation (r = 0.8 at p ≤ 0.02, and r = 0.7) between watermelon production, Tmax and rainfall during OND, especially in Unguja, as well as Tmin during JJA (i.e. r = - 0.8 at p ≤ 0.02) in Pemba. Besides, results have shown the existence of significant differences between the means of watermelon production and climate parameter for the two stated periods, indicating that the climate parameters highly affects the watermelon production by either enhancing or declining the yields by 69% - 162% and 17% - 77%, respectively. Moreover, results have shown that respondents were aware that excess temperature intensity during dry periods can lead to high production costs due number of soil and other environmental factors. Besides the results have shown that OND seasonal rainfall and MAM Tmax had good association with watermelon production in Unguja while JJA Tmin declined the production in Pemba. Thus, the study concludes that seasonal variability of climate parameter has a significant influence on the watermelon production. The study calls for more studies on factors affecting watermelon production (e.g. soil characteristics, pest sides and manure), and recommends for climate based decision making on rain fed agricultural yields and routine monitoring of weather information.