Annual Peak Research Articles

On the stochastic significance of peaks in the least-squares wavelet spectrogram and an application in GNSS time series analysis

In this paper, the mathematical derivation of the underlying probability distribution function for the normalized least-squares wavelet spectrogram is presented. The impact of empirical and statistical weights on the estimation of the spectral peaks and their significance are demonstrated from the statistical point of view both theoretically and practically. The simulation results show an improvement of approximately 0.02mm (RMSE) for annual signal estimation when statistical weights are considered in the least-squares wavelet analysis (LSWA). The weighted LSWA estimates the signals more accurately than the ordinary LSWA for different percentage amount of missing data. As a real-world application, Global Navigation Satellite Systems (GNSS) time series for a station in Rome, Italy are analyzed. The analyses of the GNSS time series provided by different agencies for the same station reveal statistically significant annual peaks, more significant in 2010 but less significant between 2018 and 2020, while the higher frequency components show different spectral patterns over time. A declining trend of approximately −0.42 mm/year since 2004 is estimated for the GNSS height time series, likely due to gradual land subsidence. The results not only highlight the advantages of LSWA but can also help to better understand the uncertainties involved in signal estimation.

Investigating Catchment‐Scale Daily Snow Depths of CMIP6 in Canada

AbstractAccurate modeling of snow depth (SD) processes is critical for understanding global energy balance changes, affecting climate change mitigation strategies. This study evaluates the Coupled Model Intercomparison Project Phase 6 (CMIP6) model performance in simulating daily SD across Canada. We assess CMIP6 outputs against observed data, focusing on daily SD averages, snow cover durations, and rates of accumulation and depletion, alongside annual SD peaks for 11 major Canadian catchments. Our findings reveal that CMIP6 simulations generally overestimate daily SD by 57.7% and extend snow cover duration by 30.5 days on average. While three models (CESM2, UKESM1‐0‐LL and MIROC6) notably align with observed annual SD peaks, simulation biases suggest the need for enhanced model parameterization to accurately capture snow physics, particularly in regions with permanent snow cover and complex terrains. This analysis underscores the necessity of refining CMIP6 simulations and incorporating detailed geographical data for better SD predictions.

Performance evaluation of underground thermal storage integrated dual-source heat pump systems

The increasing demand for electricity stresses the existing electric grids. Buildings consume 73% of all U.S. electricity and are responsible for 30% of U.S. greenhouse gas emissions. Integrating thermal energy storage (TES) in building heating/cooling systems, which consume considerable electricity, can mitigate the challenges to electric grids. This study reports on a novel thermal energy storage device integrated heat pump system to reshape the building electricity demand profile while maintaining thermal comfort. The annual performance of the proposed system has been evaluated through a dynamic system simulation with high fidelity in the Modelica platform. The dynamic model of the novel hybrid component named ‘dual purpose underground thermal battery’ was developed and validated. It was then incorporated into the system model. Given a time-of-use tariff, a rule-based control strategy was designed to shift the electric demand and switch the heat pump source for a typical single-family house in different climate zones of the United States. The system performance of the new TES-integrated dual-source heat pump was compared with that of a conventional air-source heat pump system. The results indicate that the proposed system can reduce the annual HVAC electricity cost by up to 52% while saving 45.2% on electricity consumption. In the Northern areas, the annual peak load of the HVAC system can be reduced by 64.9%. However, this reduction is less in the Southern areas as the system’s higher efficiency in winter dominates the overall energy-saving potential.

The snow cover is more important than other climatic variables on the prediction of vegetation dynamics in the Pyrenees (1981–2014)

The dynamics of the mountain vegetation is governed by multiple climatic drivers including temperature, precipitation, radiation and snow cover variability. However, in the Mediterranean environment, little is known about the relative importance of each variable. In this study we assess how different snowpack indices (the maximum annual accumulation, the length of the snow season, and the melt-out date) and key climate variables (precipitation, temperature and shortwave solar radiation) control the interannual variability of the maximum Normalized Difference Vegetation Index (peak NDVI) in the Pyrenees. We use a 33 year long remote sensing dataset (1981–2014) to build a statistical model relating the annual peak NDVI with snow and climate variables. In elevated areas characterized by a well developed seasonal snowpack the melt-out date was the most important climatic variable for predicting the annual peak NDVI. However, at lower elevations where snow presence is ephemeral, shortwave solar radiation was the most important variable. This change in the relative importance of climatic variables occurs around 1300 m a.s.l. The results do not show a significant contribution of maximum snow accumulation, suggesting that indicators of snow presence (i.e. melt-out date or snow season duration), which are significantly easier to obtain than snow mass indicators from remote sensing, could be used to model the influence of the snowpack on peak NDVI at regional scale.

Sentinel Enhanced Dengue Surveillance System - Puerto Rico, 2012-2022.

Dengue is the most prevalent mosquitoborne viral illness worldwide and is endemic in Puerto Rico. Dengue's clinical spectrum can range from mild, undifferentiated febrile illness to hemorrhagic manifestations, shock, multiorgan failure, and death in severe cases. The disease presentation is nonspecific; therefore, various other illnesses (e.g., arboviral and respiratory pathogens) can cause similar clinical symptoms. Enhanced surveillance is necessary to determine disease prevalence, to characterize the epidemiology of severe disease, and to evaluate diagnostic and treatment practices to improve patient outcomes. The Sentinel Enhanced Dengue Surveillance System (SEDSS) was established to monitor trends of dengue and dengue-like acute febrile illnesses (AFIs), characterize the clinical course of disease, and serve as an early warning system for viral infections with epidemic potential. May 2012-December 2022. SEDSS conducts enhanced surveillance for dengue and other relevant AFIs in Puerto Rico. This report includes aggregated data collected from May 2012 through December 2022. SEDSS was launched in May 2012 with patients with AFIs from five health care facilities enrolled. The facilities included two emergency departments in tertiary acute care hospitals in the San Juan-Caguas-Guaynabo metropolitan area and Ponce, two secondary acute care hospitals in Carolina and Guayama, and one outpatient acute care clinic in Ponce. Patients arriving at any SEDSS site were eligible for enrollment if they reported having fever within the past 7 days. During the Zika epidemic (June 2016-June 2018), patients were eligible for enrollment if they had either rash and conjunctivitis, rash and arthralgia, or fever. Eligibility was expanded in April 2020 to include reported cough or shortness of breath within the past 14 days. Blood, urine, nasopharyngeal, and oropharyngeal specimens were collected at enrollment from all participants who consented. Diagnostic testing for dengue virus (DENV) serotypes 1-4, chikungunya virus, Zika virus, influenza A and B viruses, SARS-CoV-2, and five other respiratory viruses was performed by the CDC laboratory in San Juan. During May 2012-December 2022, a total of 43,608 participants with diagnosed AFI were enrolled in SEDSS; a majority of participants (45.0%) were from Ponce. During the surveillance period, there were 1,432 confirmed or probable cases of dengue, 2,293 confirmed or probable cases of chikungunya, and 1,918 confirmed or probable cases of Zika. The epidemic curves of the three arboviruses indicate dengue is endemic; outbreaks of chikungunya and Zika were sporadic, with case counts peaking in late 2014 and 2016, respectively. The majority of commonly identified respiratory pathogens were influenza A virus (3,756), SARS-CoV-2 (1,586), human adenovirus (1,550), respiratory syncytial virus (1,489), influenza B virus (1,430), and human parainfluenza virus type 1 or 3 (1,401). A total of 5,502 participants had confirmed or probable arbovirus infection, 11,922 had confirmed respiratory virus infection, and 26,503 had AFI without any of the arboviruses or respiratory viruses examined. Dengue is endemic in Puerto Rico; however, incidence rates varied widely during the reporting period, with the last notable outbreak occurring during 2012-2013. DENV-1 was the predominant virus during the surveillance period; sporadic cases of DENV-4 also were reported. Puerto Rico experienced large outbreaks of chikungunya that peaked in 2014 and of Zika that peaked in 2016; few cases of both viruses have been reported since. Influenza A and respiratory syncytial virus seasonality patterns are distinct, with respiratory syncytial virus incidence typically reaching its annual peak a few weeks before influenza A. The emergence of SARS-CoV-2 led to a reduction in the circulation of other acute respiratory viruses. SEDSS is the only site-based enhanced surveillance system designed to gather information on AFI cases in Puerto Rico. This report illustrates that SEDSS can be adapted to detect dengue, Zika, chikungunya, COVID-19, and influenza outbreaks, along with other seasonal acute respiratory viruses, underscoring the importance of recognizing signs and symptoms of relevant diseases and understanding transmission dynamics among these viruses. This report also describes fluctuations in disease incidence, highlighting the value of active surveillance, testing for a panel of acute respiratory viruses, and the importance of flexible and responsive surveillance systems in addressing evolving public health challenges. Various vector control strategies and vaccines are being considered or implemented in Puerto Rico, and data from ongoing trials and SEDSS might be integrated to better understand epidemiologic factors underlying transmission and risk mitigation approaches. Data from SEDSS might guide sampling strategies and implementation of future trials to prevent arbovirus transmission, particularly during the expansion of SEDSS throughout the island to improve geographic representation.

The Mechanism of Socio-Spatial Evolution in Rural Areas Driven by the Development of the Planting Industry—A Case Study of Yuezhuang Village in Shandong Province, China

Rural industrialisation in China, which has historically been restricted by urban–rural dual development policies, has been largely encouraged since the Rural Revitalisation Strategy released in 2018. Industrialisation has deeply influenced socio-spatial evolution by providing job opportunities and promoting the construction of infrastructure and public services, accelerating the two-way flow of urban–rural factors in rural areas. However, this may lead to social and spatial issues, such as social inequalities and unsuitable living conditions, to some extent. This research aims to investigate how the socio-spatial evolution of rural areas was influenced by rural industrialisation and the two-way flow of urban–rural factors. The cherry planting industry and an active “industrial centre”, Yuezhuang Village, in Linqu County, Shandong Province, China, were selected for the empirical study. This research analysed the evolution of the cherry planting industry, the formulation of rural social networks, and the spatial adaptation of the two using mixed qualitative and quantitative methods, followed by a discussion on the current rural planning policies in China. The results show that the cherry industrial chain largely extended the planting areas, and the categories of land use generally increased in Yuezhuang Village over the 30 years studied. The average output value per mu (mu is unit of area measurement used in China, where it is officially standardised. It corresponds to 1/15 of a hectare, or about 666.67 m2). In 2021 was 25 times larger than that in 1996. The population involved in industrial activities increased to over 5000 people countrywide from the initial 17 households. The results also show that urban–rural factors, such as capital, population, techniques, and information, rapidly accelerated in the annual peak season of the cherry industry. This research indicates that small-scale, characteristic, and traditional agriculture could become one of the main driving forces in urban–rural integration and may contribute to sustainable and inclusive rural communities and urban–rural partnerships. This provides the theoretical mechanism of socio-spatial interaction in rural industrialisation in China based on the empirical case study.

Peak shaving at system level with a large district heating substation using deep learning forecasting models

The decarbonisation of urban district heating (DH) systems requires increased heating grid flexibility. Therefore, this article examines the optimised operation of a tank thermal energy storage (TTES) on the secondary side of a new DH substation for an industrial site in a German city, in order to shave the peaks of the whole DH system and thus reduce the need for heat-only boilers (HOB). The accuracy of heat load and return temperature forecasts for both the industrial consumer and the DH grid is critical to the performance of the optimisation-based operating strategy of the TTES. Therefore, long short-term memory neural networks are used in combination with continuous model updates through incremental learning to create two forecasting scenarios, one using only preceding data for the forecasts and the other including future weather data. The results show that high forecasting accuracy is most relevant for reducing the annual maximum peak, with a reduction of 2.8% in the preceding data scenario, 4% with future weather data and 7% in a benchmark with perfect forecasts. The economic viability of the storage through HOB heat savings is primarily affected by lower forecasting accuracy when the additional cost of HOB heat is less than 60 €/MWh.

Relationships between snowpack, low flows and stream temperature in mountain watersheds of the US west coast

AbstractWater temperatures in mountain streams are likely to rise under future climate change, with negative impacts on ecosystems and water quality. However, it is difficult to predict which streams are most vulnerable due to sparse historical records of mountain stream temperatures as well as complex interactions between snowpack, groundwater, streamflow and water temperature. Minimum flow volumes are a potentially useful proxy for stream temperature, since daily streamflow records are much more common. We confirmed that there is a strong inverse relationship between annual low flows and peak water temperature using observed data from unimpaired streams throughout the montane regions of the United States' west coast. We then used linear models to explore the relationships between snowpack, potential evapotranspiration and other climate‐related variables with annual low flow volumes and peak water temperatures. We also incorporated previous years' flow volumes into these models to account for groundwater carryover from year to year. We found that annual peak snowpack water storage is a strong predictor of summer low flows in the more arid watersheds studied. This relationship is mediated by atmospheric water demand and carryover subsurface water storage from previous years, such that multi‐year droughts with high evapotranspiration lead to especially low flow volumes. We conclude that watershed management to help retain snow and increase baseflows may help counteract some of the streamflow temperature rises expected from a warming climate, especially in arid watersheds.

Exploring the impact of cardiologists expertise on publication metrics

BackgroundIn the field of academic cardiology, the assessment of an author's scholarly impact and professional progression heavily relies on publications and citations. This study investigates whether specific cardiology expertise correlates with accelerated professional growth. MethodsUsing data from the 2023 European Society of Cardiology congress, 948 faculty attendees with an h-index of 30 or higher were analyzed. Expertises were categorized into six groups, and their association with publications and citations peaks was explored. ResultsInterventional cardiologists exhibited the highest annual publication peak, followed by imaging and electrophysiology experts. However, no significant differences were observed in citation peaks among expertise groups. While imaging experts initially appeared to reach citation peaks faster, this effect diminished after statistical adjustments. Additionally, holding multiple expertise areas prolonged the time to reach publication and citation peaks by approximately six years. ConclusionThis study underscores the influence of expertise in interventional cardiology on publication peaks but suggests that citation peaks and career progression velocity remain unaffected by expertise type. Furthermore, it highlights that holding multiple areas of expertise slowers the attainment of career peak for scholarly authors.

Passive acoustic monitoring and convolutional neural networks facilitate high-resolution and broadscale monitoring of a threatened species

Population monitoring is an essential component of biodiversity conservation and management, but low detection probabilities for rare and/or cryptic species makes estimating abundance and occupancy challenging. Passive acoustic monitoring combined with machine learning algorithms represents a potential path forward to effectively and efficiently monitor the occurrence of rare vocalizing species across entire forest landscapes. Our objectives were to develop and implement a convolutional neural network (PNW-Cnet) to identify vocalizations of a rare and threatened forest nesting bird species – the marbled murrelet (Brachyramphus marmoratus) – in the Pacific Northwest, U.S.A., 2018–2021. We used PNW-Cnet predictions from broadscale passive acoustic monitoring data to examine spatiotemporal patterns in the distribution of murrelets. PNW-Cnet showed sufficiently high prediction accuracy (overall precision > 0.9) to enable broadscale population monitoring. Spatiotemporal analysis showed that annual peak murrelet call abundance occurs in ordinal weeks 28–32 (late July–Mid August) but this varied by study area. The greatest number of detections typically occurred in the Olympic Peninsula and Oregon Coast Range where late-successional forest dominates and nearer to ocean habitats. We demonstrate that passive acoustic monitoring can be used to understand intensity of use across broad scales for a rare and cryptic species in addition to the typical detection/non-detection data that are often collected. Passive acoustic monitoring combined with PNW-Cnet offers considerable promise for species distribution modeling and long-term population monitoring for rare species.

Flood frequency analysis using mean daily flows vs. instantaneous peak flows

Abstract. In many cases, flood frequency analysis (FFA) needs to be carried out on mean daily flows (MDF) instead of instantaneous peak flows (IPF), which can lead to underestimation of design flows. Typically, correction methods are applied to the MDF data to account for such underestimation. In this study, we first analyse the error distribution of MDF-derived flood quantiles over 648 catchments in Germany. The results show that using MDF instead of IPF data can lead to underestimation of the mean annual peak flow (MHQ) by up to 80 % and mainly depends on the catchment area but appears to be influenced by gauge elevation as well. This relationship is shown to differ for summer vs. winter floods. To correct such underestimation, different linear models based on predictors derived from MDF hydrograph and catchment characteristics are investigated. Apart from the catchment area, a key predictor in these models is the event-based ratio of flood peak to flood volume (p/V ratio) obtained by the MDF data. The p/V models applied to either MDF-derived events or statistics seem to outperform other reference correction methods. Moreover, they require a minimum data input, are easily applied, and are valid for the entire study area. The best results are achieved when the L moments of the MDF maximum annual series are corrected with the proposed model, which reduces the flood quantile errors by up to 60 %. The approach behaves particularly well in smaller catchments (<500 km2), where reference methods fall short. However, the limit of the proposed approach is reached for catchment sizes under 100 km2, where the hydrograph information from the daily series is no longer capable of approximating instantaneous flood dynamics and gauge elevations below 100 m, where the difference between MDF and IPF floods is very small.

Characterizing future changes in compound flood risk by capturing the dependence between rainfall and river flow: An application to the Yangtze River Basin, China

Compound floods induced by co-occurring multiple drivers may exacerbate the flood impacts and lead to larger flood damage. Exploring future changes in compound flood risk is imperative for flood management and disaster reduction. This study attempts to investigate future changes in compound flood risk across the Yangtze River Basin during 2030 ∼ 2100. Future river flow was projected using an improved hydrological model and pairwise series of extremes of rainfall and river flow were extracted from both observed and projected series. Subsequently, stochastic pairs of rainfall and river flow characterizing compound floods were proportionally sampled from their bivariate joint distributions. The damage from each compound flood was obtained from the flood damage function constructed by Random Forests (RF). Further, the expected annual damage (EAD) was calculated to investigate future changes in compound flood risk. Results show that: (1) Future annual maximums of rainfall and river flow are expected to increase by 14.51 % ∼ 66.13 % and 1.72 % ∼ 55.73 % in the mainstream and northern tributaries, while future annual peak discharge in the southern tributaries (except for the Dongting Lake Basin) is expected to decrease by 4.18 % ∼ 12.30 %. A similar spatial distribution of future changes is also found in the bivariate joint distribution of rainfall and river flow. (2) The high coefficient of determination (R2) of 0.84 indicates the satisfactory simulation and projection capacity of the constructed flood damage function. The positive stepped relationship between flood damage and rainfall or river flow reflects the superposition of multiple flood damage processes. (3) The Han River Basin, the Jialing River Basin, and the two-lakes (the Dongting and Poyang Lakes) area face great threat from compound floods in both historical and future periods. Future compound flood risk is expected to increase by 13.43 % ∼ 46.04 % in these regions except for the Poyang Lake Basin, while future risk is expected to increase by 2.03 % ∼ 46.04 % in the whole basin. The findings help improve the understanding of future flood risk variations in the Yangtze River Basin and provide essential information for damage reduction.

Long Term Forecasting of Peak Demand and Annual Electricity Consumption of the West African Power Pool Interconnected Network by 2032

The uneven distribution of primary sources of electric power generation in Economic Community of West African States (ECOWAS) compelled the heads of states to create the West African Power Pool (WAPP). The vision of this system is to set up a common electrical energy market to satisfy the balance between supply and demand at an affordable price using the interconnected network. Forecasting maximum power demand and energy consumption is essential for planning and the coordination of new power plant and transmission lines building. This work consists of predicting maximum power demand and total energy that must transit through the WAPP interconnected network by the year 2032. We compare the performances of three time series models namely the Long Short-Term Memory (LSTM), Auto-Regressive Integrated Moving Average (ARIMA) and Fb Facebook Prophet. Electric power and energy data used for training the systems comes from the WAPP authorties. The results show that, for monthly peaks, the Facebook (Fb) Prophet model is the best, with a MAPE (mean absolute error percentage) of 3.1% and a low RMSE (root mean square error) of 1.225 GW. For energy prediction, ARIMA performances are the best compared to others with (RMSE 1.20 TWh, MAPE 1.00%). Thus, the forecast for total annual energy consumption and annual peak demand will be, respectively, 96.85TWh and 13.6 GW in 2032.

Optimizing energy solutions: A techno-economic analysis of solar-wind hybrid power generation in the coastal regions of Bangladesh

Hybrid renewable energy systems have acquired attention worldwide for their ability to harness multiple renewable sources parallelly like solar, wind, and hydropower, presenting numerous advantages. Bangladesh is forced to rely on the traditional fossil fuel-powered power generation infrastructure in order to meet the nation’s increasing need for electricity. The goal of this paper is to improve the percentage of renewable energy in Bangladesh’s energy landscape by addressing the technical, economic, and environmental elements of building a specialized hybrid system at Patenga Sea Beach through a methodical approach. This study is the first to pinpoint and address Patenga Sea Beach’s limits in light of Shah Amanat International Airport’s nearby location in Chittagong. In order to build the suggested system, a daily load demand of 1000 kWh and an annual peak demand of 53.57 kW were taken into account. The analysis shows that this system has a levelized cost of energy (LCOE) of 0.03$, generating 4,604.591 MWh of power annually. Findings from HOMER reveal an initial capital outlay of $350,688 for initial capital investment, with an annual O&M cost of $3,821, contributing to its cost-effectiveness. During the span of its lifetime, the hybrid system can also avoid 5,767 tons of CO2. The simulation program PVsyst has been utilized to assess the PV system-specific performance, viability, and outcomes. According to the PVsyst evaluation, the PV system has an 89 % system performance ratio, a 0.017$ levelized cost of energy, and a break-even period of only 6.6 years. The sensitivity analysis examines renewable resources, factoring in climate change’s effects on solar irradiation, wind speed, replacement cost, and operational expenses. It validates the hybrid system’s viability across various environmental scenarios, demonstrating resilience to changes in renewable resource costs and availability. The suggested hybrid system can be placedto minimize costs associated with energy generation, circumvent structural and geographic limitations, improve the Patenga Sea beach’s visual appeal, and lower greenhouse gas emissions. In addition to supporting future decisions about the deployment of hybrid systems, this research could assist in the integration of sustainable and renewable energy sources into the national grid.

Flood Risk Assessment of the Middle Reach of the Helmand River, Afghanistan

The middle reach of the Helmand River is prone to floods, and flood hazard analysis is necessary to manage the flood risk. As the study site is a less-developed basin where available geographical and hydrological data are limited, we must utilize available limited data to conduct the flood hazard analysis. A frequency analysis of the annual peak flow rate was performed using available hydrological data for making representative flood hydrographs. A land-use map is derived by classifying recent Landsat images. Digital elevation model derived by Advanced Land Observing Satellite, roughness coefficient correlated with the land-use map, and the representative flood hydrographs are fed into hydraulic model HEC-RAS 2D. Flood inundation area, depth, and velocity for different flood return periods are estimated. Based on local hazard intensity and vulnerability, risk maps for evacuation and vulnerability of crops on the floodplain are obtained. Spatial flood risk characteristics differ at the upstream and downstream reaches because of distinct floodplain topography. Flood countermeasures for each reach are proposed based on the spatial characteristics of the flood risk. To provide risk information for residents within the region, the flood hazard, risk, and damage maps derived in this study are published as a web map application and a mobile native application.

FOCA: a new quality-controlled database of floods and catchment descriptors in Italy

Abstract. Here we present FOCA (Italian FlOod and Catchment Atlas), the first systematic collection of data on Italian river catchments for which historical discharge time series are available. Hydrometric information, including the annual maximum peak discharge and average daily annual maximum discharge, is complemented by several geomorphological, climatological, extreme rainfall, land-cover and soil-related catchment attributes. All hydrological information derives from the most recently released datasets of discharge and rainfall measurements. To enhance the reproducibility and transferability of the analysis, this paper provides a description of all the raw data and the algorithms used to build the basin attribute dataset. We also describe the approaches adopted to solve problems encountered during the digital elevation model elaboration in areas characterized by a complex morphology. Details about the data quality-control procedure developed to detect and correct errors are also reported. One of the main novelties of FOCA with respect to other national-scale datasets is the inclusion of a rich set of geomorphological attributes and extreme rainfall features for a large set of basins covering a wide range of elevations and areas. Using this first nationwide data collection (available at https://doi.org/10.5281/zenodo.10446258, Claps et al., 2023), a wide range of environmental applications, with a particular focus on flood studies, can be undertaken within the Italian territory.

Small forested watershed weathers effects of climate change better than a nearby urban watershed in Northern Virginia, USA

AbstractSouth Fork Quantico Creek (SFQ; 19.8 square kilometre (km2), forested) and Fourmile Run (4MR; 32.4‐km2, urban) are small watersheds in northern Virginia, United States. Precipitation and streamflow data for both watersheds were examined from water year (WY) 1952 through 2022. Temporal changes in hydrologic metrics were identified by calculating trends in annual precipitation, annual peak flow, mean daily flow, minimum daily flow, stream flashiness, and the runoff ratio. The impact of climate and urbanization on watershed hydrology was assessed by computing trends on both raw and precipitation‐adjusted data. Despite increasing precipitation in both watersheds, increasing monotonic trends in most hydrologic metrics were observed only in 4MR. At 4MR, the long‐term trend in annual peak flow was non‐linear, thus trends were calculated on separate periods. Annual peak flow increased from WY 1952 through 1968, coinciding with a period of rapid urbanization. During WY 1969 through 1981, annual peak flows decreased, coinciding with construction of a flood channelization project. Trends for both periods were robust to precipitation adjustment. From WY 1982 through 2022, no change in the precipitation‐adjusted annual peak flows occurred, suggesting annual peak flows increased due to climate factors during this period. Comparison of area‐normalized hydrologic metrics between the two watersheds revealed higher flows in 4MR than SFQ across all flows, not just high flows. Runoff ratio and stream flashiness also were higher in 4MR. Differences in hydrologic metrics between the two watersheds were driven primarily by differences in land use, land cover, and modifications to the water balance related to urbanization. Climate change has altered watershed hydrology at both sites, but extensive urbanization in 4MR has altered the hydrology more than that of SFQ. We conclude that urban watersheds are likely at greater risk of increased flooding than less developed areas as the climate intensifies.

Micro-Catchments, Macro Effects: Natural Water Retention Measures in the Kylldal Catchment, Germany

Floods are among the most devastating and financially burdensome natural disasters in Europe. The combined impact of climate change and land use change is expected to exacerbate and intensify the destructive consequences of river floods. In this study, we analysed the effects of wetland restoration on peak and base flows and on water quality in the Kylldal catchment of the Kyll River in the German Middle Mountains using the Soil and Water Assessment Tool+ (SWAT+). Monthly median daily discharge increases varied between 3% and 33% in the studied (micro)catchments. The higher median flow rates show that discharge peaks were attenuated and distributed over a longer period, making both extreme peak flows and low flows less common. Peak flows tended to decrease, with the largest effects between late fall and early spring when peak flow values decreased by up to 18%. The annual maximum peak flows in each of the three micro-catchments decreased by 12–24% on average. The occurrence of daily average flow rates larger than 1 m3 s−1 was up to 45% lower after wetland restoration. Low flows increased by up to 21% and 13% in the summer and fall, respectively, which suggests that drought risk also decreases after wetland restoration. Average nitrogen exports decreased by 38–50% in the project areas and by 20% at the catchment level. Average phosphorus exports decreased by 52–67% in the project areas and by 25% at the catchment level. The study highlights the potential of wetland restoration for improving hydrological services, mitigating flood risks, and enhancing water quality. Restoring and maintaining freshwater ecosystems and their natural sponge functions is crucial for effectively managing water resources and addressing the challenges posed by climate change and land use changes.

Dynamics of Puccinia striiformis f. sp. tritici Urediniospores and Its Relationship with Meteorological Factors in Mianyang, China.

Stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is one of the main diseases of wheat worldwide. Mianyang of Sichuan province in Southwest China is one of main regions for winter Pst inoculum production and spring epidemic and provides urediniospores for infecting wheat in the surrounding regions. Understanding the urediniospore dynamics is important to predict and manage stripe rust. In this study, spore trapping coupled with a TaqMan real-time quantitative PCR method was used to monitor airborne Pst urediniospores from December 2019 to December 2022 in Mianyang. Weather conditions (temperature, relative humidity, daily sunshine duration, and precipitation) were collected for the same period. These data were used to study the relationship of airborne urediniospore density with climatic conditions. The results showed that Pst urediniospores were captured all year round, and the annual peak of urediniospore densities occurred in the period from March to April in which the urediniospores accounted for the largest proportion of the annual total urediniospores. The density of urediniospores in the period of March to April was linearly related to the average sunshine duration of 20 days and average temperature of 15 days prior to the final day of a 7-day trapping period. This relationship needs to be tested in other regions where Pst can sporulate during the winter before it can be integrated with Pst infection conditions to predict rust development.

Trends in airborne grass pollen in Évora City (Portugal)

Aerobiology could be used as complementary data or proxy for studying phenology, especially in species which usually are not long-distance transported, such as grasses. This 21-year aerobiological study took place in Évora (South Portugal), using a seven-day recording volumetric pollen trap with the aim of analysing the temporal trends of grass pollen seasons. To this end, data were statistically tested for correlation and regression to determine the features and temporal trends of pollen seasons. Main results show that Poaceae pollen has a high representation in pollen spectrum, 20% (min: 7%; max: 44%), with a long season starting in March and lasting till August/September or October. There was an overall, but not statistically significant trend, towards an increase in the annual pollen integral and peak values, and also an overall non-statistically significant trend towards an earlier start and later end of the pollen season. Main and high pollen seasons have been lasting longer, and the days with allergenic potential have been rising in line with the local temperature rising trend. It seems that grass pollen production is being aided by warmer temperatures and increased grassland areas in Alentejo region. This could promote the increase in annual pollen levels and in the number of high pollen days over the years. In clinical terms, it means that grass allergic patients are exposed to longer periods of airborne pollen and to higher counts over a longer time. The climate conditions projected for Alentejo region, including rising temperature, and the changes in local grassland areas may help to clarify the trends of grass pollen seasons in the next decades.