Complex Geography Research Articles

The geography of avoided deforestation and sustainable forest management offsets: the enduring question of additionality

ABSTRACT Avoided deforestation and sustainable forest management (AD/SFM) offset projects have developed piecemeal, lacking consensus standards and receiving only quite low carbon prices. The diverse forces project developers face have led to a complex geography of different offsetting standards and funding sources adopted by various projects. Combining and updating existing AD/SFM pilot project databases, we produce – to our knowledge – the largest publicly available dataset of such projects’ approximate locations, containing nearly 800 worldwide. Using Cox proportional hazards models, we analyze the geography of these AD/SFM offset projects to understand better how different pressures project developers face in this weakly consolidated field have shaped where projects are more and less likely to be undertaken. We find that, at a fifty-kilometre-per-side hexagonal resolution, both AD/SFM projects tend to be located in areas with high above-ground biomass and high IUCN Red List species richness. Additionally, AD projects tend to be located in areas with stronger deforestation drivers. There is also a clear geographic specialization, with over 85% of the SFM projects in our dataset located in the United States. In contrast, AD projects have generally been established in countries that have engaged with the United Nations Reducing Emissions from Deforestation and Forest Degradation (UN-REDD) Programme or the World Bank’s Forest Carbon Partnership Facility. While AD/SFM projects may not be avoiding emissions at levels estimated in existing offset systems, they might still have a role to play in climate and habitat protection.

Impact of Ambient Temperature on Mortality Burden and Spatial Heterogeneity in 16 Prefecture-Level Cities of a Low-Latitude Plateau Area in Yunnan Province: Time-Series Study.

The relation between climate change and human health has become one of the major worldwide public health issues. However, the evidence for low-latitude plateau regions is limited, where the climate is unique and diverse with a complex geography and topography. This study aimed to evaluate the effect of ambient temperature on the mortality burden of nonaccidental deaths in Yunnan Province and to further explore its spatial heterogeneity among different regions. We collected mortality and meteorological data from all 129 counties in Yunnan Province from 2014 to 2020, and 16 prefecture-level cities were analyzed as units. A distributed lagged nonlinear model was used to estimate the effect of temperature exposure on years of life lost (YLL) for nonaccidental deaths in each prefecture-level city. The attributable fraction of YLL due to ambient temperature was calculated. A multivariate meta-analysis was used to obtain an overall aggregated estimate of effects, and spatial heterogeneity among 16 prefecture-level cities was evaluated by adjusting the city-specific geographical characteristics, demographic characteristics, economic factors, and health resources factors. The temperature-YLL association was nonlinear and followed slide-shaped curves in all regions. The cumulative cold and heat effect estimates along lag 0-21 days on YLL for nonaccidental deaths were 403.16 (95% empirical confidence interval [eCI] 148.14-615.18) and 247.83 (95% eCI 45.73-418.85), respectively. The attributable fraction for nonaccidental mortality due to daily mean temperature was 7.45% (95% eCI 3.73%-10.38%). Cold temperature was responsible for most of the mortality burden (4.61%, 95% eCI 1.70-7.04), whereas the burden due to heat was 2.84% (95% eCI 0.58-4.83). The vulnerable subpopulations include male individuals, people aged <75 years, people with education below junior college level, farmers, nonmarried individuals, and ethnic minorities. In the cause-specific subgroup analysis, the total attributable fraction (%) for mean temperature was 13.97% (95% eCI 6.70-14.02) for heart disease, 11.12% (95% eCI 2.52-16.82) for respiratory disease, 10.85% (95% eCI 6.70-14.02) for cardiovascular disease, and 10.13% (95% eCI 6.03-13.18) for stroke. The attributable risk of cold effect for cardiovascular disease was higher than that for respiratory disease cause of death (9.71% vs 4.54%). Furthermore, we found 48.2% heterogeneity in the effect of mean temperature on YLL after considering the inherent characteristics of the 16 prefecture-level cities, with urbanization rate accounting for the highest proportion of heterogeneity (15.7%) among urban characteristics. This study suggests that the cold effect dominated the total effect of temperature on mortality burden in Yunnan Province, and its effect was heterogeneous among different regions, which provides a basis for spatial planning and health policy formulation for disease prevention.

Geography of symplectic Lefschetz fibrations and rational blowdowns

We produce simply-connected, minimal, symplectic Lefschetz fibrations realizing all the lattice points in the symplectic geography plane below the Noether line. This provides a symplectic extension of the classical works populating the complex geography plane with holomorphic Lefschetz fibrations. Our examples are obtained by rationally blowing down Lefschetz fibrations with clustered nodal fibers, the total spaces of which are potentially new homotopy elliptic surfaces. Similarly, clustering nodal fibers on higher genera Lefschetz fibrations on standard rational surfaces, we get rational blowdown configurations that yield new constructions of small symplectic exotic 4 4 –manifolds. We present an example of a construction of a minimal symplectic exotic C P 2 # 5 C P ¯ 2 {\mathbb {CP}}{}^{2}\# \, 5 \overline {\mathbb {CP}}{}^{2} through this procedure applied to a genus– 3 3 fibration.

Assessment of Rainfall and Temperature Trends in the Yellow River Basin, China from 2023 to 2100

China’s Yellow River Basin (YRB) is sensitive to climate change due to its delicate ecosystem and complex geography. Water scarcity, soil erosion, and desertification are major challenges. To mitigate the YRB’s ecological difficulties, climate change must be predicted. Based on the analysis of the evolution features of hydro-meteorological elements, the CMIP6 climate model dataset with Delta downscaling and the Empirical Orthogonal Function (EOF) is utilized to quantitatively explore the future variations in precipitation and temperature in the YRB. The following results are drawn: The spatial resolution of the CMIP6 climate model is less than 0.5° × 0.5° (i.e., about 55 km × 55 km), which is improved to 1 km × 1 km by the downscaling of Delta and has outstanding applicability to precipitation and temperature in the YRB. The most accurate models for monthly mean temperature are CESM2-WACCM, NorESM2-LM, and ACCESS-CM2, and for precipitation are ACCESS-ESM1-5, CESM2-WACCM, and IPSL-CM6A-LR. Between 2023 and 2100, annual precipitation increases by 6.89, 5.31, 7.02, and 10.18 mm/10a under the ssp126, ssp245, ssp370, and ssp585 climate scenarios, respectively, with considerable variability in precipitation in the YRB. The annual temperature shows a significant upward trend, and the change rates under the different climate scenarios are, respectively, 0.1 °C/10a, 0.3 °C/10a, 0.5 °C/10a, and 0.7 °C/10a. The increase is positively correlated with emission intensity. Based on the EOF analysis, temperature and precipitation mainly exhibit a consistent regional trend from 2023 to 2100, with the primary modal EOF1 of precipitation for each scenario exhibiting a clear spatial distribution in the southeast–northwest.

Pilot and data power optimization problems in multiple Aerial Relay Stations cell‐free communication systems

SummaryA cell‐free (CF) technology and unmanned aerial vehicles (UAVs) acting as aerial relay stations (ARSs) are gradually viewed as viable technologies for usage in sixth‐generation (6G) wireless networks owing to their outstanding advantages, such as large coverage, uniform service quality, huge connections, flexible deployment in all geographical areas, and high line‐of‐sight (LoS) probability. As a result, the combination of the CF model and UAVs is suitable for densely populated urban areas, shopping malls, festivals, and locations with complex geography. Furthermore, the integration of the CF model and UAV communication can improve system quality and is considered an entirely new research direction as there is no comprehensive investigation of this combined model. In this study, we investigate the quality of the multi‐ARS CF communication system, where ARSs are equipped with multiple antennas and stochastic distribution within a specific region to simultaneously serve numerous ground users. We establish a closed‐form formulation for the uplink and downlink throughput of individual users by using the matched filtering method and conjugate beamforming technology, respectively. Moreover, we propose a novel method to optimize the pilot and data transmission power coefficients for improving channel estimation and increasing throughput per user, thereby ensuring a high quality of service. The power optimization method is executed via the successive convex approximation (SCA) method, second‐order cone program (SOCP), and linear program. We evaluate system performance according to the cumulative distribution function (CDF) of user throughput based on various parameters, such as the number of users, the number of ARSs, and the length of pilot sequences. The analytical findings also reveal that the system with the proposed power optimization method is always superior to the system without the proposed method in both uplink and downlink.

Effect of trauma quality improvement initiatives on outcomes and costs at community hospitals: A scoping review

BackgroundDue to complex geography and resource constraints, trauma patients are often initially transported to community or rural facilities rather than a larger Level I or II trauma center. The objective of this scoping review was to synthesize evidence on interventions that improved the quality of trauma care and/or reduced healthcare costs at non-Level I or II facilities. MethodsA scoping review was performed to identify studies implementing a Quality Improvement (QI) initiative at a non-major trauma center (i.e., non-Level I or II trauma center [or equivalent]). We searched 3 electronic databases (MEDLINE, Embase, CINAHL) and the grey literature (relevant networks, organizations/associations). Methodological quality was evaluated using NIH and JBI study quality assessment tools. Studies were included if they evaluated the effect of implementing a trauma care QI initiative on one or more of the following: 1) trauma outcomes (mortality, morbidity); 2) system outcomes (e.g., length of stay [LOS], transfer times, provider factors); 3) provider knowledge or perception; or 4) healthcare costs. Pediatric trauma, pre-hospital and tele-trauma specific studies were excluded. ResultsOf 1046 data sources screened, 36 were included for full review (29 journal articles, 7 abstracts/posters without full text). Educational initiatives including the Rural Trauma Team Development Course and the Advanced Trauma Life Support course were the most common QI interventions investigated. Study outcomes included process metrics such as transfer time to tertiary care and hospital LOS, along with measures of provider perception and knowledge. Improvement in mortality was reported in a single study evaluating the impact of establishing a dedicated trauma service at a community hospital. ConclusionsOur review captured a broad spectrum of trauma QI projects implemented at non-major trauma centers. Educational interventions did result in process outcome improvements and high rates of self-reported improvements in trauma care. Given the heterogeneous capabilities of community and rural hospitals, there is no panacea for trauma QI at these facilities. Future research should focus on patient outcomes like mortality and morbidity, and locally relevant initiatives.

Religion and lived space: A geographical reading of La Religion de près

Spatial approaches to religion have grown significantly in recent years and offer original perspectives for understanding new religious realities in the context of secularized countries. This article is an opportunity to show the richness of this “spatial turn” through an analysis of the theoretical tools developed by the French anthropologist Albert Piette in his work La religion de près: l’activité religieuse en train de se faire. Although this work, which has a connection to actor-network theory, does not deal directly with space, it exhibits a geographical sensibility that we will reveal. We show that these tools can be used to demonstrate a complex geography of contemporary religion—in particular the existence of a socio-spatial dialectic—and illustrate their fruitfulness with the case of an ethnographic study of two Quebec evangelical churches.

Assessing the role of the United Nations in countering terrorism in Africa: A case study of the Lake Chad Basin

ABSTRACT This study examines the United Nations’ counterterrorism efforts in the Lake Chad Basin (LCB), with a focus on the persistent challenges posed by the Boko Haram insurgency. Using a critical analysis approach, the research appraises the legal, policy, and empirical dimensions of the UN’s counterterrorism strategies. It delves into the evolution of terrorism in the LCB, the emergence of Boko Haram, and the socio-economic and geopolitical factors contributing to the issue. The study argues that the UN and the Multinational Joint Task Force’s (MNJTF) efforts in the LCB have experienced successes but face challenges like porous borders, weak governance, and complex geography. It contributes to the global terrorism discourse by highlighting the intricate challenges in implementing effective counterterrorism measures in a region with unique political, social, and environmental contexts. The study recommends a multi-pronged strategy that incorporates context-specific conditions, financial disruptions, and eco-security measures. It underscores the importance of multi-sectoral collaboration and sustained global commitment for achieving lasting peace and security in the LCB.

Real-time monitoring and diagnostics of the person’s emotional state and decision-making in extreme situations for healthcare

Monitoring and diagnosing the emotional state is important for varied professional groups, especially, those involve in hazardous work, risks, and high responsibility (pilots, astronauts, miners, sailors, firefighters, military personnel, law enforcement officers, etc.). These professions are definitely extreme. Moreover, the number of these professions and people engaged in them is constantly increasing. Unfortunately, disorders of the emotional state are not easily recognized by visible symptoms, so prompt remote diagnosis and monitoring of a person's emotional state is important, especially in conditions of complex geography, lack of transportation and mobility of patients, reduced financial resources, or lack of medical staff. In many cases, effective supports from various participants involved in a difficult situation that could become dangerous are required to make a timely decision. The authors, who have aviation experience in operational detection of deviations in the pilot's emotional state and decision making under risk, propose to apply the concept of mental activity, which is based on the property of the mind to slow down or speed up the flow of subjective time in relation to the real one, for the monitoring and diagnosing the emotional state of a person. For the timely detection of a hazardous emotional state of a person in an extreme situation, the phase plane method is used, the essence of which is to build phase trajectories for differential equations in the coordinate system. The identification of a person's emotional state in real time is based on the variance analysis of the models of spontaneous (optimal), emotional and rational activities. The deformity of the emotional state is determined using a priori person’s models based on the actual material of a posteriori research of the accident investigations. The Nyquist criterion is used to measure the functional stability of a person. The method of real-time diagnostics of the person’s emotional state is presented. A software “Diagnostics of the emotional state of a human-operator” is developed. The problem of effective monitoring and diagnostics of a person's emotional state can be solved with the help of an Intelligent Remote Monitoring System (IRMS) built on the basis of dynamic modeling principles, when the subsystems are formalized in the form of transfer functions. A person is considered as a control object, and the monitoring and diagnostics of the emotional state are based on the analysis of the phase portrait obtained by the control device. The conceptual model and the functional diagram of medical IRMS are worked out. The algorithm for monitoring and diagnosing the person’s emotional state is presented. Mikhailov and Nyquist criteria are used to determine the IRMS stability; Mikhailov and Nyquist hodographs are built. IRMS is proposed for monitoring the emotional state of a person in medicine, sports, treatment, and for automated monitoring of persons in hazardous environments, for example, in an aeroplane (passengers), in a smart home (people), in medicine (patients), etc. Prompt monitoring and diagnosis enables timely adjustment and improvement of a person's emotional state and prevents the development of an extreme situation towards worsening. Based on the objective-subjective collaborative decision-making method under uncertainty, the problem of urgent delivery of medical cargo using UAV from the point of departure to the destination is solved. The optimal decision with minimum risk and maximum safety according to the Wald-Wald, Wald-Laplace, and Wald-Hurwitz criteria and with the consideration of all participants’ opinions on UAV transportation, is found. Based on the dynamic programming method, the task of finding a route with minimum cost is solved when it is necessary to urgently deliver medicines to a seriously ill patient using UAV.

Using Big Data for the Construction of an Intelligent Region

The modern world is characterized by a growth in the amount of data generated and collected. “Big data” provides opportunities for improving life and efficiency in various spheres. Creating smart cities where technology enhances the quality of life and service efficiency is an important direction in the use of big data. However, the use of digitization should not only concern places with a high population density. The answer to the challenge of digitizing populated areas of small size but relatively high population density is the creation of an intelligent region. The current technological environment is changing approaches to the management and development of regions. This is especially true for places with complex geography, a multinational community, and diverse economic sectors, such as Transcarpathia. This article explores the possibility of creating an intelligent region in Transcarpathia using modern methods of big data processing.

Abundance, Characteristics, and Microplastics Load in Informal Urban Drainage System Carrying Intermixed Liquid Waste Streams

This first-of-its-kind study systematically assesses the abundance and characteristics of Microplastics (MPs) in different categories of informal open drains (nallas) carrying different liquid waste streams from different functional areas of an Indian city. Such drains are part of the informal urban drainage system that carries wastewater, stormwater, industrial effluent, and rural runoff. Logistical and locational limitations of traditional wastewater (WW) sampling methods severely limit their application in open drains. To overcome sampling challenges owing to complex geography, vast drainage network spread across different functional areas of the entire city, and local challenges, appropriately modified sampling strategies were adopted to collect samples from 35 open WW drains (small/local, intermediatory, and large). MPs (50μm-5mm) were present in a bucket, and net samples obtained from all 35 WW drains. The average MP concentration in WW drains was 4.20 ± 1.40 particles/L (bucket samples) and 5.19 ± 1.32 particles/L (net samples). A declining trend of MPs abundance was observed from larger to smaller drains, confirming that smaller and intermediatory drains (carrying WW from different functional areas of the city) are discharging their MP loads into larger drains. Intermixing different WW streams (municipal WW, stormwater surface runoff, agricultural runoff, and industrial WW) increases MP levels in drains. The local riverine ecosystem is being put at risk by a daily MPs load of 12.6 x 108 particles discharged from 9 larger drains into the local river Kharun. To protect the riverine ecosystem, controlling the high daily MPs load from such drains is important. Diversion of WW drains through constructed wetlands built near river banks can be a cost-effective solution. Because the entire Indian subcontinent and parts of Africa rely mainly on such drains having similar characteristics and local conditions, the findings of this study reflect the status and pattern of MPs pollution in informal drains of the entire Indian subcontinent and can be used by stakeholders and governments to take mitigative and preventive measures to manage the MPs pollution and protect the local riverine ecosystem.

Seasonal analysis of land surface temperature using local climate zones in peak forest basin topography: A case study of Guilin

Understanding the characteristics of Land Surface Temperature (LST) is crucial to mitigate the Surface Urban Heat Island (SUHI) effect. Local Climate Zones (LCZs) framework can be applied in LST analysis and SUHI quantification. However, this framework primarily focuses on evaluating detailed local-scale SUHI, thereby failing to provide a comprehensive assessment of the overarching SUHI trends at the urban level. Additionally, existing LST analyses ignored geographic factors, resulting in partial analysis of LST, especially in areas with complex geography. To fill these gaps, the study develops a SUHI assessment method combining an improved surface urban heat intensity ratio index with LCZs-based SUHII, enabling a dual evaluation of both overall and detailed SUHI effect. The impact of geographic factors, both topography and natural features, on seasonal LST patterns is investigated, especially within built LCZs. Additionally, the spatio-temporal image fusion model is employed to address issues of cloud effects or image mutilations in LST results. Guilin, with unique peak forest basin topography, is selected as a case study to represent complex geographical areas. The results reveal the following: (1) An abnormal heat island phenomenon occurs during winter due to Guilin's distinctive geomorphological conditions. (2) Cooling effects influenced by mountains and water bodies on different built LCZs exhibit seasonal variation, with the strongest cooling effect observed within a 50-m distance. LCZ 2 consistently exhibits the strongest cooling effect. The study offers valuable insights for guiding decisions in sustainable urban planning and design, especially in cities influenced by complex local geography.

Using a hybrid approach for wind power forecasting in Northwestern Mexico

Wind energy is an important renewable source that has been considerably developed recently. In order to obtain successful 24-h lead-time wind power forecasts for operational and commercial uses, a combination of physical and statistical models is desirable. In this paper, a hybrid methodology that employs a numerical weather prediction model (Weather Research and Forecasting) and a neural network (NN) algorithm is proposed and assessed. The methodology is applied to a wind farm in northwestern Mexico, a region with high wind potential where complex geography adds large uncertainty to wind energy forecasts. The energy forecasts are then evaluated against actual on-site power generation over one year and compared with two reference models: decision trees (DT) and support vector regression (SVR). The proposed method exhibits a better performance with respect to the reference methods, showing an hourly normalized mean absolute percentage error of 6.97%, which represents 6 and 13 percentage points less error in wind power forecasts than with DT and SVR methods, respectively. Under strong synoptic forcing, the NN wind power forecast is not very accurate, and novel approaches such as hierarchical algorithms should be employed instead. Overall, the proposed model is capable of producing high-quality wind power forecasts for most weather conditions prevailing in this region and demonstrates a good performance with respect to similar models for medium-term wind power forecasts.

Compound events of wet and dry extremes: Identification, variations, and risky patterns

Compound hydrometeorological extremes have been widely examined under climate change, they have significant impacts on ecological and societal well-being. This study sheds light on a new category compound of contrasting extremes, namely compounding wet and dry extremes (CWDEs). The CWDEs are characterized as devastating dry events (EDs) accompanied by wet extremes (EWs) in a given time window. Notably, we first adopt a separate system to identify coinciding events considering the different evolving processes and impacting patterns of EDs and EWs. The peak-over-threshold and standardized index methods are used in a daily and monthly window to identify EWs and EDs respectively. Furthermore, the spatial-temporal changes and risky patterns of CWDEs are revealed by using the Mann-Kendall test, the Ordinary Least Squares, and the Global and Local Moran indices. Germany is the study case. As one major finding, the results indicate a pronounced seasonal effect and spatial clustering pattern of CWDEs. The summer is the most vulnerable period for CWDEs, and the spatial hotspots are mainly located in the southern tip of Germany, as well as in the vicinity of the capital city Berlin. Besides, robust uptrends in CWDEs across all evaluation metrics have been discovered in historical periods, and the moist climate and complex geography collectively contribute to severe CWDEs. Unexpectedly, the study finds that compounding events in dry regions are mainly driven by wet extremes, whereas they show a higher dependency on dry anomalies in wet regions. The research provides new insights into compound extremes which are composed of individual hazards with distinct features. Related findings will aid decision-makers in producing effective risk mitigation plans for prioritizing vulnerable regions. Lastly, the robust framework and open access data allow for extensive exploration of various compounding hazards in different regions.

The struggle for problematising housing (in Italy): Reflections from Naples, Turin and beyond

During the last 15 years, amid the global impacts of the economic crisis, austerity politics, and increasing centrality of real estate and construction for capitalism, housing has taken central stage in public and political debates globally. In Italy, the trajectory of housing politicisation has been quite peculiar. Despite a longstanding tradition of housing conflict and a complex geography of new mobilisations, housing has remained at the margins of national discussions. Seen through the lens of Foucauldian problematisation analysis, housing has not been acknowledged as a ‘problem’ in Italy. This article engages with the Italian peculiarity through a comparative study of housing problematisation—operationalised through the dimensions of framing, coalitions and scale. I compare two cities where analogous challenges intersect with very different housing regimes and political contexts: in Turin, the capacity of short-term housing policies to address pressing problems is mirrored by a lack of engagement by institutional and politicised actors; in Naples, in a housing regime defined by informal solutions, the relations among the local authorities and social movements have oscillated among dialogue, conflict, institutionalisation and pacification. By putting these cases in conversation through a generative, relational and multi-scalar lens, I discuss housing problematisation in a country, Italy, characterised by deep regional asymmetries, regionally-specific housing regimes and a complex geography of housing conflict. By reconsidering the national case vis-à-vis broader dynamics, in conclusion, I also provide some takeaways for a reflection on the conditions, preconditions and efforts for scaling up the housing struggle.

Estimating the Aboveground Biomass of Various Forest Types with High Heterogeneity at the Provincial Scale Based on Multi-Source Data

It is important to improve the accuracy of models estimating aboveground biomass (AGB) in large areas with complex geography and high forest heterogeneity. In this study, k-nearest neighbors (k-NN), gradient boosting machine (GBM), random forest (RF), quantile random forest (QRF), regularized random forest (RRF), and Bayesian regularization neural network (BRNN) machine learning algorithms were constructed to estimate the AGB of four forest types based on environmental factors and the variables selected by the Boruta algorithm in Yunnan Province and using integrated Landsat 8 OLI and Sentinel 2A images. The results showed that (1) DEM was the most important variable for estimating the AGB of coniferous forests, evergreen broadleaved forests, deciduous broadleaved forests, and mixed forests; while the vegetation index was the most important variable for estimating deciduous broadleaved forests, the climatic factors had a higher variable importance for estimating coniferous and mixed forests, and texture features and vegetation index had a higher variable importance for estimating evergreen broadleaved forests. (2) In terms of specific model performance for the four forest types, RRF was the best model both in estimating the AGB of coniferous forests and mixed forests; the R2 and RMSE for coniferous forests were 0.63 and 43.23 Mg ha−1, respectively, and the R2 and RMSE for mixed forests were 0.56 and 47.79 Mg ha−1, respectively. BRNN performed the best in estimating the AGB of evergreen broadleaved forests; the R2 was 0.53 and the RMSE was 68.16 Mg ha−1. QRF was the best in estimating the AGB of deciduous broadleaved forests, with R2 of 0.43 and RMSE of 45.09 Mg ha−1. (3) RRF was the best model for the four forest types according to the mean values, with R2 and RMSE of 0.503 and 52.335 Mg ha−1, respectively. In conclusion, different variables and suitable models should be considered when estimating the AGB of different forest types. This study could provide a reference for the estimation of forest AGB based on remote sensing in complex terrain areas with a high degree of forest heterogeneity.

Assessment of IMERG v06 Satellite Precipitation Products in the Canadian Great Lakes Region

Abstract The Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM; IMERG) is a high-resolution gridded precipitation dataset widely used around the world. This study assessed the performance of the half-hourly IMERG v06 Early and Final Runs over a 5-yr period versus 19 high-quality surface stations in the Great Lakes region of North America. This assessment not only looked at precipitation occurrence and amount, but also studied the IMERG Quality Index (QI) and errors related to passive microwave (PMW) sources. Analysis of bias in accumulated precipitation amount and precipitation occurrence statistics suggests that IMERG presents various uncertainties with respect to time scale, meteorological season, PMW source, QI, and land surface type. Results indicate that 1) the cold season’s (November–April) larger relative bias can be mitigated via backward morphing; 2) IMERG 6-h precipitation amount scored best in the warmest season (JJA) with a consistent overestimation of the frequency bias index − 1 (FBI-1); 3) the performance of five PMW sources is affected by the season to different degrees; 4) in terms of some metrics, skills do not always enhance with increasing QI; 5) local lake effects lead to higher correlation and equitable threat score (ETS) for the stations closest to the lakes. Results of this study will be beneficial to both developers and users of IMERG precipitation products. Significance Statement The purpose of the study was to assess the performance of the gridded precipitation product from the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) version 6 over the Great Lakes region of North America. The assessment performs a statistical comparison of precipitation amounts from IMERG versus surface stations as a function of time scale, season, precipitation event threshold, and input source among satellites. Interpretation of the results identifies shortcomings in the IMERG algorithms, particularly in extreme precipitation events and over ice-covered surfaces. The results also describe spatial variability in the IMERG data quality due to the complex geography of the study area and offer a clear threshold in the Quality Index (QI) flag for optimal application of the precipitation products.

Spatiotemporal investigation of wet–cold compound events in Greece

Abstract. Climate change is set to affect extreme climate and meteorological events. The combination of interacting physical processes (climate drivers) across various spatial and temporal scales resulting to an extreme event is referred to as compound event. The complex geography and topography of Greece forms a variety of regions with different local climate conditions affecting the daily minimum temperature and precipitation distributions and subsequently the distribution of compound events of low temperature and high precipitation values. The aim of our study in this work is to identify these wet–cold events based on observational data from the Hellenic National Meteorological Service (HNMS) stations, which are divided into five different geographical categories, in the period 1980–2004 and coldest months of the year (November-April) on monthly basis. Two available reanalysis products, that of ERA-Interim downscaled with the Weather Research and Forecasting (WRF) model at 5km horizontal resolution (WRF_5), and the coarser resolution (∼30 km) ERA5 Reanalysis dataset from European Centre for Medium-Range Weather Forecasts (ECMWF), are adopted to derive a gridded monthly spatial distribution of wet–cold compound events, after performing a comparison with the observations. The results yield that the monthly maximum HNMS probabilities range from 0.07 % in April to 0.85 % in February, ERA5 range from 0.4 % in April to 2.97 % in February and WRF_5 from 10.4 % in November to 25.04 % in February. The results also displayed that February, January and December, are in this order, the months with the highest WCCEs.

Preventing Central Line Bloodstream Infections: An Interdisciplinary Virtual Model for Central Line Rounding and Consultation

Background: Central line–associated bloodstream infections (CLABSI) account for many harms suffered in healthcare and are associated with increased costs and disease burden. Central line rounds, like medical rounds, are a multidisciplinary bedside assessment strategy for all active central lines on a unit. In-person line rounds in this 144-bed oncology acute care setting are challenging due to a variety of unchangeable factors. The aim was to develop a process for addressing concerning central lines in this context. Methods: The project team designed a HIPAA-protected, text-based process for assessing central lines for risk factors contributing to infection. Staff initiated a consultation via a virtual platform with an interdisciplinary team composed of oncology and infectious diseases experts. The virtual discussion included recommendations for a line-related plan of care. Results: The number of consultations averaged about five per month, with 27.4% resulting in the central line being removed, which is believed to have contributed to an overall reduction in infection rates. The CLABSI standardized infection ratio, a risk-adjusted measure which accounts for patient acuity and volumes, improved from 0.85 prior to the intervention (November 2020–October 2021) to 0.57 after the intervention (November 2021–August 2022), a 33% reduction. Conclusion: A virtual process for central line consultation and interdisciplinary planning was effective and, in this setting, perhaps optimal. This type of process could be applied to nearly any aspect of clinical care where teams are solving problems in an environment with complex geography and relationships.

Spatio‐temporal patterns of ichthyoplankton in southern Chilean Patagonia: β‐diversity and associated environmental factors

AbstractFjords play an important role in biological productivity worldwide but are vulnerable to climate/anthropogenic effects. Chilean Patagonia (41°S–55°S) is one of the largest fjord ecosystems in the world, characterized by a complex geography with highly heterogeneous hydrographic conditions and a permanent input from oceanic water mass, both of which influence the ichthyoplankton distributional patterns. In this study, we analysed the distributional patterns of ichthyoplankton and its diversity during austral spring from 1996, 2009 and 2019 in the southern Chilean fjords (47°S–54°S). The area shows important latitudinal gradients of sea surface temperature (from 15°C to 5°C), fjord‐ocean variability in salinity (from 23 to 33) and water column temperature (3°C to 9°C). Spatial (but not temporal) variations in the composition and abundance of ichthyoplankton were recorded, caused mainly by differences in the abundance of larval Sprattus fuegensis, Maurolicus australis and Sebastes oculatus. Richness was higher on continental shelf and the lowest nearby fjord's head and ice fields. At the spatial scale, β‐diversity showed an increased species replacement (turnover) across areas, suggesting an important role of environmental conditions (e.g., salinity and water stratification) on the larval fish structure in this region. Ordination analysis indicates that changes in salinity and temperature, caused by ice melting, is the main environmental factor influencing the abundance of larval Thysanopsetta naresi (1996), Cataetyx messieri (2009) and Bathylagichthys parini (2019). Finally, this study highlights the importance of the conservation and protection of the Chilean Patagonia and monitoring ichthyoplankton communities, which are vital biomarkers of ecosystem health.