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This research aims at studying the intrinsic vulnerability of groundwater to diffuse environmental pollutants in the Muravera coastal agricultural area of Sardinia, Italy. The area faces contamination risks arising from agricultural practices, especially the use of fertilizers, pesticides, and various chemicals that can seep into the groundwater. The study examined the interplay among hydrological elements, including soil characteristics, groundwater depth, climate conditions, land use, and aquifer properties. To do that, the outcomes of FLOWS 1D physically-based agrohydrological model were analyzed in parallel with those of the overlay-and-index model SINTACS, in a sort of reciprocal benchmarking. By using FLOWS, water movement and solute transport in the unsaturated zone were simulated by, respectively, solving the Richard Equation (RE) and the Advection-Dispersion equation (ADE). As such, this model allowed to account for the role of soil hydraulic and hydrodispersive properties variability in determining the travel times of a conservative solute through the soil profile to the groundwater. For FLOWS simulations, a complete dataset was used as input, including soil horizons, soil physical and hydraulic properties of 36 soil profiles, average annual depth to groundwater table at each soil profile (ranging from 1 to 50 meters), and climatic temporal series data on rainfall and evapotranspiration. Detailed analyses of travel times for the movement of 25, 50, 75, and 100% of the solute mass to reach groundwater were conducted, revealing that the depth to groundwater predominantly influences vulnerability. This result was coherent with SINTACS vulnerability map due to the large impact of the depth to groundwater on SINTACS analysis. [ABSTRACT FROM AUTHOR]

Although metacommunity theory provides many useful insights for conservation planning, the transfer of this knowledge to practice is hampered due to the difficulty of identifying metacommunities in bioregions. This study aims to identify the spatial extent of metacommunities at bioregional scales using current and historical habitat data, especially because contemporary biodiversity patterns may be a result of time‐lagged responses to historical habitat configurations. Further, this estimation of the metacommunity spatial extent is based on both the habitat structure and the dispersal ability of the species. Focusing on dragonfly and damselfly (odonate) species in the eastern Swiss Plateau, the research uses wetland habitat information spanning over 110 years to create a time series of nine habitat networks between 1899 and 2010. From these networks, we identified the spatial extents of metacommunities based on the year of habitat information as well as on watershed boundaries. To identify the best metacommunity spatial extents, the study investigates whether patch pairs within a metacommunity exhibit greater similarity in species composition (i.e. lower beta‐diversity) than patch pairs between metacommunities. For the different metacommunities, we further investigated correlations between gamma diversity and metacommunity size and compare them to theoretical expectations. In both analyses we found that augmenting spatial metacommunity identification with historical geographical proximity results in stronger associations with biodiversity patterns (beta and gamma diversity) than when using only current‐day habitat or watershed information. [ABSTRACT FROM AUTHOR]

The concept of water quality has often generally revolved around the all-round safety of water for human consumption. The quality of much of the 3% of the earth's humanly consumable water classed as freshwater is under threat of climate change, rising population numbers, indiscriminate land usage, detrimental agricultural practices and contamination from poor waste management. The need for optimal water quality enhancement has become more germane to sustainable socio-economic development. This paper examines the evolution of efforts made by the scientific community over the years to ensure water quality can be characterized and properly managed to ensure the global ever-growing demand for clean water for human consumption is continually met. The development of state-of-the-art computational decision support systems (DSS) should play a vital role. However, efforts in this regard are currently bedevilled by major challenges such as quantifying, measuring, processing and controlling the numerous metrics of water quality, as well as their adaptation and integration into a fully developed universal water quality model. In addressing these challenges, a shift towards simpler modelling approaches and the integration of uni-purpose models which can be cascaded into decision-making systems is being popularly proposed. However, with technological advancements already stimulating a water quality management revolution, there is a shift in paradigm to more universal modelling attempts with great optimism towards overcoming the challenges of developing universal water quality models and DSS. The prospects and opportunities of a water quality management renaissance offered by radical scientific innovations look promising, as the world races with time to provide support systems that can help deal better with the dynamics of sustainable water supply in increasingly contaminable environments and progressively unpredictable climates. [ABSTRACT FROM AUTHOR]

Understanding specific air–water interfacial area (SAWIA) is essential for characterizing and modeling various phenomena in vadose zone hydrology, such as virus and colloid transport, contaminant dissolution, evaporation, and the hydro‐mechanical behavior of unsaturated soils. Traditional measurement methods, including X‐ray imaging and tracer techniques, often encounter challenges, leading to a scarcity of studies that provide a reliable relationship for SAWIA. Currently, no pedotransfer function in the literature links SAWIA with saturation and suction using readily measurable soil properties such as median grain size and porosity. In this study, we initially developed a pore network model capable of predicting SAWIA by calibrating it with corresponding soil‐water retention curves (SWRCs). We then used these models to compile a comprehensive database of SAWIA for six sandy soils, for which experimental SWRCs were available, covering a range of median grain sizes and porosities. Utilizing this database, we established a pedotransfer function through multigene genetic programming. The accuracy of this function was validated against experimental data not previously used in its training and testing. Our parametric study indicated that increases in either porosity or median particle size led to a decrease in the regions exhibiting higher SAWIA in terms of saturation and suction. Core Ideas: Estimating the specific air–water interfacial area in different hydraulic branches using a pore network approach.Compiling a specific interfacial area database for soils with different values of porosity and median grain size.Using multigene‐genetic programming to extract a pedotransfer function for specific interfacial area.The proposed specific interfacial area pedotransfer function requires only soil median grain size and porosity. [ABSTRACT FROM AUTHOR]

Although water availability and water quality are equally important for effective water resources management at various spatial and temporal scales, to date, a combined representation of soil water balance components and water quality components in Python is not available. The new RoGeR toolbox contains models that can be used for not only the quantification of hydrological processes, fluxes and stores, but also solute transport processes based on StorAge selection (SAS). This study presents the code structure and functionalities of RoGeR developed as a scientific model toolbox following defined open-source software guidelines. RoGeR uses five different computational backends covering just-in-time compilation, parallelism and graphical processing units (GPUs) that might be used for optimizing computational performance. We show that graphical processing unit computing has the greatest potential to improve computation time and energy usage, especially for large modelling experiments. A simple modelling experiment highlights the capabilities of the new RoGeR model toolbox. We simulated the soil water balance, stable water isotope (18 O) transport, and corresponding travel time distributions of the Eberbaechle catchment, Germany, for a 3-year period. Due to the current limitations of a variety of process components, further development of RoGeR as a scientific software is needed. Future modifications are easily possible due to the open software architecture of RoGeR. [ABSTRACT FROM AUTHOR]

The concept of climate tipping points in socio‐environmental systems is increasingly being used to describe nonlinear climate change impacts and encourage social transformations in response to climate change. However, the processes that lead to these tipping points and their impacts are highly complex and deeply uncertain. This is due to numerous interacting environmental and societal system components, constant system evolution, and uncertainty in the relationships between events and their consequences. In the face of this complexity and uncertainty, this research presents a conceptual framework that describes systemic processes that could lead to tipping points socio‐environmental systems, with a focus on coastal communities facing sea level rise. Within this context, we propose an organizational framework for system description that consists of elements, state variables, links, internal processes, and exogenous influences. This framework is then used to describe three mechanisms by which socio‐environmental tipping could occur: feedback processes, cascading linkages, and nonlinear relationships. We presented this conceptual framework to an expert panel of coastal practitioners and found that it has potential to characterize the effects of secondary climatic impacts that are rarely the focus of coastal risk analyses. Finally, we identify salient areas for further research that can build upon the proposed conceptual framework to inform practical efforts that support climate adaptation and resilience. Plain Language Summary: In the face of climate change, there is growing concern that incremental adaptation measures will be insufficient in addressing climate risks. Socio‐environmental tipping points describe situations where a small change or pressure results in a societal system moving into a fundamentally different state. This concept is increasingly used to describe risks from climate change and the meaningful societal changes necessary to reduce these risks. However, understanding how climatic tipping points might unfold in socio‐environmental systems is very challenging because these systems are highly complex, with human, built, and environmental components that interact in unpredictable ways. This research presents a framework for describing tipping points in socio‐environmental systems, and uses this descriptive framework to identify three tipping point mechanisms. These are feedback processes, cascading linkages, and nonlinear relationships. A panel of coastal practitioners reviewed the framework and found that it could capture the multiple potential tipping points that they have found in their work. By developing a common way of describing climatic tipping points in socio‐environmental systems, this framework can support comparative studies across different locations and the development of computational models for exploring the impact of tipping points and potential interventions. Key Points: The study presents a conceptual framework of climate‐related tipping points in socio‐environmental systemsIt identifies and describe three tipping mechanisms: feedback processes, cascading linkages, and nonlinear relationshipsAn expert panel of coastal practitioners validated the framework and provided multiple examples of tipping points in practice [ABSTRACT FROM AUTHOR]

Closed depressions are common landscape features across glaciated landscapes. Erosion and runoff from depression hillslopes may result in phosphorus (P) accumulation near the bottom of the depression, with this "legacy P" potentially at risk of loss to surface waters when drained via tile drainage. We assessed spatial patterns of soil P within a tile‐drained watershed in northeastern Indiana as a function of landscape position and agricultural management practices. Paired soil samples (depression bottom vs. hillslope contributing area) were collected from agricultural (n = 14) depressions at four depths (0–60 cm). Water‐extractable phosphorus (WEP), Mehlich‐3 extracted phosphorus (M3‐P), total phosphorus (TP), Hedley P fractions, and other physical and chemical characteristics were determined. To assess the risk of P loss, P desorption from surface soils (0–5 cm) was quantified using flow‐through experiments. Results showed that WEP, M3‐P, and TP were 2–10 times greater in the depression bottom compared to hillslopes across all depths. Long‐term management practices such as P application history and tillage influenced the magnitude of soil P concentration, degree of P saturation, and vertical stratification of soil P. Flow‐through experiments highlighted that the risk of P loss was highly dependent on M3‐P concentration for both hillslope and depression soils. Findings therefore indicate that closed depressions may act as hotspots for P cycling and loss in tile‐drained watersheds. Including low‐lying depressional areas as part of a routine soil sampling strategy combined with variable rate P application could lessen P accumulation in depressions and reduce P loading to surface waters. Core Ideas: Phosphorus accumulation in closed depressions of a tile‐drained watershed was assessed.WEP, Mehlich‐3 extracted phosphorus (M3‐P), and total P were significantly greater near the depression bottom compared to hillslopes.Topography and agricultural management influenced P accumulation within depressions.Phosphorus desorption during flow‐through experiments showed that P loss was dependent on M3‐P concentration.Soil sampling in depressions combined with variable rate P application could reduce the risk of P loss. [ABSTRACT FROM AUTHOR]

The mechanisms driving catchment nitrogen storage and release operate at multiple spatiotemporal scales. Consequently, analyses grounded in different observational timescales can yield discrepant interpretations of underlying mechanisms. To assess the consistency of nitrate export patterns between event‐ and inter‐annual scales, we evaluated multiple years of high‐frequency observations of nitrate concentrations (C) and discharge (Q) including 3,480 discrete discharge events from 28 dominantly agricultural catchments. We observed consistent and often drastic divergence between long‐term and median event‐specific C‐Q patterns. Most catchments showed long‐term enrichment patterns (positive C‐Q slope), but events were, on average, more chemostatic (close‐to‐zero C‐Q slopes). C‐Q slope variability was high for small events but decreased with event magnitude, approaching chemostatic patterns during the largest storms, yielding compelling evidence against nitrate source limitation. We conclude that nitrate export patterns observed at different temporal scales and event magnitudes are controlled by different processes, therefore embedding complementary information. Plain Language Summary: Storage and mobilization of nitrogen in the landscape ultimately shape nitrate concentration dynamics in streams and their transport to downstream receiving waters. However, the temporal resolution of data analyses (e.g., long‐term or high‐flow events) can affect the interpretation of nitrate export from catchments and underlying mechanisms. To test whether nitrate export patterns differ between event and long‐term time scales, we evaluated several years of daily observations of nitrate concentration and discharge, including 3,480 high‐flow events from 28 dominantly agricultural catchments. We observed consistent and often drastic divergence between long‐term and average event‐specific export patterns. The variability in export patterns between events decreased with event magnitude toward patterns that indicate no nitrogen source limitation. Nitrate export patterns are regulated by different controls depending on the temporal scale of analysis, which must be considered to avoid incomplete and misleading interpretations about the underlying mechanisms. Key Points: Long‐term and event‐scale nitrate export patterns diverge, pointing at complementary drivers of catchment functioning across time scalesEvent‐scale C‐Q slopes decreased in variability with event magnitude and showed no signs of source limitation at high‐magnitude eventsMulti‐catchment high‐frequency data revealed the crucial role of time scale and event magnitude for interpreting nitrate export patterns [ABSTRACT FROM AUTHOR]

Floodplain societies decide whether to protect themselves against floods (fight), live with floods (adapt), or adopt an approach that represents some combination of the two. The formation of a levee system is an important factor in determining whether a society fights or adapts to floods; however, these factors have been considered fixed boundaries in previous studies in human–flood interactions. We analyze a levee system transformation process covering the past century, from the indigenous ring-type levee system with floods to modern continuous levees against floods in the Kiso River basin in Japan by applying a historical sociohydrological approach. The results show degradation processes of the indigenous levee system and traditional communities alongside the installation of modern continuous levees, and a trade-off relationship was observed between the lengths of both. There are interactions between the levee systems and the human–water system through various water uses and different-scale components, and the dynamics within the region are connected to external socioeconomic trends through the installed modern levees and institutions. [ABSTRACT FROM AUTHOR]

Copyright of Brazilian Journal of Production Engineering / Revista Brasileira de Engenharia de Produção is the property of Brazilian Journal of Production Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A growing body of empirical evidence shows that experiences of nature provide people with diverse psychological benefits, including improved cognitive function and mental health. While our understanding of the proximate causes of these positive psychological responses of humans to nature has advanced, the ultimate (evolutionary) drivers behind them remain poorly understood.In this study, we provide a summary of several widely accepted evolutionary hypotheses originating from Biophilia, reviewing their challenges and limitations. Subsequently, we propose a new evolutionary psychological hypothesis—the greenery hypothesis—that integrates recent findings in evolutionary psychiatry with many experimental results unexplained by existing hypotheses/theories.Our proposed hypothesis states that humans have adapted to periodic severe drought and re‐watering cycles by developing both negative and positive psychological responses to the absence or presence of greenery within the landscape as cues to optimise their own behavioural activity.The greenery hypothesis holds the potential to yield several insights into the fundamental understanding of human psychological responses to nature exposure with significant implications for various related fields, including psychiatry, urban planning, and biodiversity conservation and restoration. Read the free Plain Language Summary for this article on the Journal blog. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

Spatially distributed properties of the subsurface result in varying water saturation and preferential flow paths, which lead to heterogeneous solute transport patterns and heterogeneous microbial environments. This, in turn, influences the distribution of nutrients and energy gradients, microbial biomass, and activity thereof. By their very nature, current field sampling techniques do not resolve subsampling scale heterogeneities in microbial biomass and activity, resulting in inaccurate estimates of microbially mediated carbon and nitrogen turnover in the heterogeneous subsurface. Thus, in this study, we undertook a numerical modeling approach to study the impact of spatial heterogeneity on microbially mediated carbon and nitrogen turnover in the vadose zone. We adapted an established biogeochemical process network that captures a variety of respiration pathways, carbon decomposition strategies, and microbial life processes to simulate microbially mediated carbon and nitrogen turnover in variably saturated spatially heterogeneous settings, using an established numerical tool (OGS#BRNS). The fractionation of microbial communities into active and inactive states, as well as immobile and mobile states followed could be linked to the bulk average saturation. Lastly, we identified three reactive systems, distinguished by the rate ratio of aerobic respiration and transfer of oxygen from the air to the water phase, to evaluate the impact of spatial heterogeneity on carbon and nitrogen removal in subsurface heterogeneous domains. Specifically, when this ratio is approximately 1, there is no impact on carbon removal, while when this ratio is very high, then carbon removal decreases as the domain tends to be oxygen limited. Core Ideas: Spatial heterogeneity leads to spatial variation of saturation in the vadose zone.Spatial heterogeneity leads to variation in the distribution of nutrients in the subsurface.The saturation and flow regime of a subsurface system influence the play‐off between respiration and aeration.Aeration may cause a homogenization effect in spatially heterogeneous domains.The location and activity of microbes can be predicted using average saturation and flow regime. [ABSTRACT FROM AUTHOR]

Monitoring soil quality index (SQI) and soil organic carbon (SOC) stock status of the Western Ghats (WG) forests in India is crucial for providing vital ecosystem services alongside sustainable forest management practices. However, comprehensive profile data on SQI and SOC stock across different forest types under WG forests are limited. The study evaluated SQI and SOC stock under three forest types, i.e. tropical wet evergreen (TWE), tropical semi-evergreen (TSE), and tropical moist deciduous (TMD) across WG in Karnataka. SQI was assessed using principal component analysis with two indexing approaches and scoring methodologies, with weightage indexing through nonlinear scoring functions (NLSF) showing superiority over other methodologies. TMD forests exhibited the highest SQI, followed by TWE and TSE, while the lowest was observed in Rippon Pet RF (0.36 surface, 0.28 control section), primarily due to limitations in organic carbon and clay content. SOC stock mirrored SQI trends (TMD > TWE > TSE), with the highest values in Kollegal RF (339.3 MG ha -1 ) and lowest in Rippon Pet RF (102.5 MG ha -1 ). Although SOC and SQI were established to be ideal indicators for dynamic ecosystem services (ESs), high OC content in surface soils of Poomale NF induces pedogenic acidification and Al toxicities, indicating potential forest soil degradation. Significant correlation with control section SQI and SOC (p < 0.05) emphasises monitoring subsurface soil status to identify soil degradation, sustainable forestry practices, and complex ESs in forest systems., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

As the frequency and intensity of heatwaves will continue to increase in the future, accurate and high-resolution mapping and forecasting of human outdoor thermal comfort in urban environments are of great importance. This study presents a machine-learning-based outdoor thermal comfort model with a good trade-off between computational cost, complexity, and accuracy compared to common numerical urban climate models. The machine learning approach is basically an emulation of different numerical urban climate models. The final model consists of four submodels that predict air temperature, relative humidity, wind speed, and mean radiant temperature based on meteorological forcing and geospatial data on building forms, land cover, and vegetation. These variables are then combined into a thermal index (universal thermal climate index – UTCI). All four submodel predictions and the final model output are evaluated using street-level measurements from a dense urban sensor network in Freiburg, Germany. The final model has a mean absolute error of 2.3 K. Based on a city-wide simulation for Freiburg, we demonstrate that the model is fast and versatile enough to simulate multiple years at hourly time steps to predict street-level UTCI at 1 m spatial resolution for an entire city. Simulations indicate that neighbourhood-averaged thermal comfort conditions vary widely between neighbourhoods, even if they are attributed to the same local climate zones, for example, due to differences in age and degree of urban vegetation. Simulations also show contrasting differences in the location of hotspots during the day and at night. [ABSTRACT FROM AUTHOR]

Large sample datasets are transforming the catchment sciences, but there are few off-the-shelf stream water chemistry datasets with complementary atmospheric deposition, streamflow, meteorology, and catchment physiographic attributes. The existing CAMELS (Catchment Attributes and Meteorology for Large-sample Studies) dataset includes data on topography, climate, streamflow, land cover, soil, and geology across the continental US. With CAMELS-Chem, we pair these existing attribute data for 516 catchments with atmospheric deposition data from the National Atmospheric Deposition Program and water chemistry and instantaneous discharge data from the US Geological Survey over the period from 1980 through 2018 in a relational database and corresponding dataset. The data include 18 common stream water chemistry constituents: Al, Ca, Cl, dissolved organic carbon, total organic carbon, HCO 3 , K, Mg, Na, total dissolved N, total organic N, NO 3 , dissolved oxygen, pH (field and lab), Si, SO 4 , and water temperature. Annual deposition loads and concentrations include hydrogen, NH 4 , NO 3 , total inorganic N, Cl, SO 4 , Ca, K, Mg, and Na. We demonstrate that CAMELS-Chem water chemistry data are sampled effectively across climates, seasons, and discharges for trend analysis and highlight the coincident sampling of stream constituents for process-based understanding. To motivate their use by the larger scientific community across a variety of disciplines, we show examples of how these publicly available datasets can be applied to trend detection and attribution, biogeochemical process understanding, and new hypothesis generation via data-driven techniques. [ABSTRACT FROM AUTHOR]

This study aims to investigate and identify the current conditions regarding the role of Sharia microfinance institutions in developing MSME businesses. The community needs Microfinance institutions, especially low-income groups and small and micro-entrepreneurs who have yet to be reached by banking financial services, especially public banks. In providing loans to MSMEs, microfinance institutions must effectively educate and equip their clients with relevant entrepreneurial knowledge and skills. This research will use a qualitative approach with a case study method, which does not use statistical generalizations but uses logical generalizations and replication. Three Sharia microfinance institutions and sixteen MSME cases will be selected in this research. The research was conducted at Sharia Microfinance Institutions as providers of microcredit and MSMEs receiving microcredit in the Southern region of the Special Region of Yogyakarta, Gunungkidul, and Kulonprogo. This research will use three data collection methods, namely interviews, observation, and documentation, to maintain the validity and reliability of the data. Data triangulation is used to test the validity of the data. The data analysis process in this research uses two stages: descriptive analysis and case analysis. The findings of this research show hope regarding the promise of easy access to capital or funding sources as an effective tool for improving MSME business development through effectiveness, supervision, and guidance carried out by Sharia microfinance institutions. This change can be realized if the community has economic stability obtained through easy access to capital, increasing business income, and empowering the community to be ready to become entrepreneurs. It supports the development of a creative economy industry based on entrepreneurship and globally competitive ethics. [ABSTRACT FROM AUTHOR]

Contributions to social capital, risk awareness, and preparedness constitute the parameters against which applications of digital technologies in the field of disaster risk management should be tested. We propose here an evaluation of four of these: mobile positioning data, social media crowdsourcing, drones, and satellite imaging, with an additional focus on acceptability and feasibility. The assessment is carried out through a survey disseminated among stakeholders. The frame of the analysis also grants the opportunity to investigate to what extent different methodologies to aggregate and evaluate the results, i.e., the Criteria Importance Through Criteria Correlation (CRITIC) model, the (Euclidean)-distance Criteria Importance Through Criteria Correlation (dCRITIC) model, the entropy model, the mean weight model, and the standard deviation model, may influence the preference of one technology over the others. We find that the different assumptions on which these methodologies rely deliver diverging results. We therefore recommend that future research adopt a sensitivity analysis that considers multiple and alternatives methods to evaluate survey results. [ABSTRACT FROM AUTHOR]

Copyright of Brazilian Journal of Management / Revista de Administração da UFSM is the property of Brazilian Journal of Management / Revista de Administracao da UFSM and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)