Estimation of the Carrying Capacity and Relative Stocking Density of Mongolian grasslands under various adaptation scenarios

Mongolia's vast grasslands, crucial for both environmental and economic stability, are currently facing challenges due to overgrazing, climate change, and land-use changes. Understanding and effectively managing their Carrying Capacity (CC) and Relative Stocking Density (RSD) is essential for maintaining ecological balance. This study rigorously evaluates the CC and RSD of Mongolia's grasslands through an innovative approach that integrates ecological models with socio-economic data, aimed at improving grazing management practices. Data from the National Agency for Meteorology and Environmental Monitoring validates the model, providing precise CC and RSD estimates at the Soum level from 2000 to 2019. The study reveals significant regional variations in CC: northern grasslands exhibit a high CC of 2.8 Sheep Units (SU) per hectare, contrasting with the fragile CC in some southern regions, like the Gobi Desert, where it is as low as 0.3 SU per hectare. Approximately 38.8 % of Mongolia's territory maintains a CC exceeding 1.0 SU per hectare, indicative of sustainable grasslands. In contrast, 41.7 % of the land, primarily in southern regions, shows CCs below 0.5 SU per hectare, highlighting ecosystem vulnerability. The RSD, reflecting livestock numbers relative to CC, averages 1.07, suggesting a high livestock concentration near Ulaanbaatar but a more sustainable density across 43.2 % of the country. The research also explores adaptation scenarios against desertification and degradation, as well as improving pasture accessibility, providing insights for future grassland management strategies. In conclusion, this study emphasizes the need for sustainable land management practices to balance carrying capacity and stocking rates, offering a vital tool for policymakers and stakeholders in grassland conservation.

The variations of SIkJalpha model for COVID-19 forecasting and scenario projections

We proposed the SIkJalpha model at the beginning of the COVID-19 pandemic (early 2020). Since then, as the pandemic evolved, more complexities were added to capture crucial factors and variables that can assist with projecting desired future scenarios. Throughout the pandemic, multi-model collaborative efforts have been organized to predict short-term outcomes (cases, deaths, and hospitalizations) of COVID-19 and long-term scenario projections. We have been participating in five such efforts. This paper presents the evolution of the SIkJalpha model and its many versions that have been used to submit to these collaborative efforts since the beginning of the pandemic. Specifically, we show that the SIkJalpha model is an approximation of a class of epidemiological models. We demonstrate how the model can be used to incorporate various complexities, including under-reporting, multiple variants, waning of immunity, and contact rates, and to generate probabilistic outputs.

Centralization of wastewater treatment in a tourist area: A comparative LCA considering the impact of seasonal changes

Nowadays, environmental protection has become a topic of primary importance, and the interest in wastewater treatment plants (WWTPs) has increased due to the need for a paradigm shift from linear to circular economy. The centralization level of wastewater infrastructure is the basis for a successful system. The aim of this study was to investigate the environmental impacts generated from the centralized treatment of wastewater in a tourist area in central Italy. The combined use of BioWin 6.2 simulation software and life cycle assessment (LCA) methodology was implemented to evaluate the potential connection of a small decentralized WWTP to a medium-size centralized facility. Two different scenarios (decentralized system, corresponding to the current situation, and centralized) were evaluated in two separate periods: high season (HS), corresponding to the main tourist season, and low season (LS), which is the period before the main tourist season. Two sensitivity analyses were conducted, assuming different N2O emission factors, and considering the period at the end of tourist season, respectively. Although with modest advantages (up to -6 % in pollutant emissions), WWTP connection was the best management option in 10 out of 11 indicators in HS, and 6 out of 11 categories in LS. The study showed that wastewater centralization was promoted by scale factors in HS, as the most impactful consumptions decreased as the degree of centralization increased; on the other hand, the decentralized system was less penalized in LS, as small WWTP was less stressed and energy consuming in this period. Sensitivity analysis confirmed the results obtained. Site-specific conditions can lead to conflicting circumstances, as key parameters may have different behaviors depending on seasonal variations, and the degree of centralization in tourist areas should be addressed by distinguishing separate periods, based on changes in tourist flows and pollution loads.

The ban on the sale of new petrol and diesel cars: Can it help control prospective marine pollution of polycyclic aromatic hydrocarbons (PAHs) in Shandong Province, China?

The constantly increasing amount of road vehicles causes massive exhaust emissions of pollutants, including polycyclic aromatic hydrocarbons (PAHs), necessitating a global responsibility to implement the policy of the ban on the sale of new petrol and diesel cars. Here, we assessed the policy control efficiency on marine pollution of PAHs in China through scenario modeling and prediction models, based on pollution monitoring, risk assessment, and source apportionment of PAHs in typical bays of Shandong Province. The results showed that in 2021, the pollution risk levels were relatively low (HI: 0.008-0.068, M-ERM-Q: 0.001-0.016, IBR: 1.23-2.69, ILCR: 8.11 ×10-6-1.99 ×10-5), and PAHs were mainly derived from traffic emissions (24.9%-35.2%), coal combustion (25.2%-32.9%), petroleum (17.2%-28.9%), and biomass combustion (17.6%-22.8%). In 2050, the predicted decrease of pollution risk values after the implementation of the policy was significant (12%-26%), and the gap between 2021 and 2050 was also significantly huge (18%-85%) without considering possible substitution of conventional energy. Collectively, this study built systematic approaches for assessing prospective marine pollution of PAHs. However, due to the particularity of Shandong Province, i.e., its national predominance of conventional energy consumption, the policy may be more effective when it comes to other coastal areas worldwide, calling for a larger scale research.

Scenario Design for Infectious Disease Projections: Integrating Concepts from Decision Analysis and Experimental Design

Across many fields, scenario modeling has become an important tool for exploring long-term projections and how they might depend on potential interventions and critical uncertainties, with relevance to both decision makers and scientists. In the past decade, and especially during the COVID-19 pandemic, the field of epidemiology has seen substantial growth in the use of scenario projections. Multiple scenarios are often projected at the same time, allowing important comparisons that can guide the choice of intervention, the prioritization of research topics, or public communication. The design of the scenarios is central to their ability to inform important questions. In this paper, we draw on the fields of decision analysis and statistical design of experiments to propose a framework for scenario design in epidemiology, with relevance also to other fields. We identify six different fundamental purposes for scenario designs (decision making, sensitivity analysis, value of information, situational awareness, horizon scanning, and forecasting) and discuss how those purposes guide the structure of scenarios. We discuss other aspects of the content and process of scenario design, broadly for all settings and specifically for multi-model ensemble projections. As an illustrative case study, we examine the first 17 rounds of scenarios from the U.S. COVID-19 Scenario Modeling Hub, then reflect on future advancements that could improve the design of scenarios in epidemiological settings.

Soil erosion risk for farming futures: Novel model application and validation to an agricultural landscape in southern England

Increasingly, agricultural land managers are seeking new approaches for understanding the potential challenges posed by sediment connectivity across catchments from source to sink, and implications for delivery of ecosystem services determined by the condition of natural capital assets. Connectivity indices have been frequently applied in the calculation of risk in spatial and temporal assessment frameworks, and tools which facilitate rapid modelling and mapping of soil erosion risk using broad-scale environmental data are therefore of considerable interest. One such indicative tool is SCIMAP (Sensitive Catchment Integrated Mapping and Analysis Platform), which highlights where sediment runoff is likely to occur and be delivered to a watercourse by simulating the generation of saturation-excess overland flow. In this paper, we examine the utility of SCIMAP for exploring the changing nature of soil erosion risk as a function of land use change in the lower Rother catchment in West Sussex, southern England through the formulation of a suite of foresight scenarios informed by knowledge of historical land cover conditions and current management practice. The study area has previously been investigated at the field scale in terms of locating and quantifying sources of erosion and areas where in-stream sedimentation manifests. Output risk values from all simulations were quantified, mapped and compared to highlight areas of greatest/lowest risk. An area was identified immediately north of the main Rother channel that consistently exhibited greatest risk across each land cover scenario. We explore (i) the spatial and temporal variation in modelled risk and (ii) the utility value of SCIMAP for agricultural land-managers and policy-makers in generating robust risk estimates of soil erosion and in-stream sedimentation, and challenges with model verification in a foresight context.

Scenario modeling to predict changes in land use/cover using Land Change Modeler and InVEST model: a case study of Karaj Metropolis, Iran

Models for land cover/land use simulation are appropriate and important tools for decision-makers, helping them build future plausible landscape scenarios. Due to the fact that the simulation results of different models may be different, it is sometimes difficult for users to choose a suitable model. Therefore, in this study, an integrated approach is used, combining the data obtained from remote sensing and GIS with Land Change Modeler (LCM) and Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) models to simulate and predict land cover/land use changes for 2028 in Karaj metropolis (Northern Iran as a poor region-in terms of data-which is under intense and rapid urbanization. In this sense, three land cover/land use maps related to the study area were primarily generated using satellite image data for the period 2006, 2011, and 2017. They were used as a basis to define two scenarios: business-as-usual (BAU) scenario and participatory plausible scenario (PPS) for 2028. Afterwards, the necessary input data used in running of both models were prepared and, then, the outputs of the models were interpreted and compared. According to the results, while human-made coverage and low-density grasslands increased by about 74% and 12%, respectively, it was from 2006 to 2017 that agricultural lands, gardens, and high-density grasslands decreased by 42%, 34%, and 7%, respectively. According to the business-as-usual scenario, which was projected using the LCM model, the increase in human-made cover will continue by about 29% by 2028, and the reduction rate of agricultural lands, gardens, and low-dense and dense grasslands will experience decrease by about 20%, 3%, 11%, and 9%, respectively. The participatory plausible scenario for 2028, which was defined using the InVEST model, confirmed the same results, but having different quantities. Accordingly, while human-made cover will increase by about 73%, the reduction rate of agricultural lands, gardens, and low-dense and dense grasslands will decrease by about 41%, 10%, 16%, and 1%, respectively. The output quantities of InVEST scenario model seem to be closer to reality with less uncertainty, because this model estimates the quantity of demand for land and its suitability for different uses, based on the views of different stakeholders, and considers landscape development future policies and plans. In contrast, the LCM model is based solely on trend extrapolation from the past to current time and changes in the landscape structure.

Supplementing Environmental Assessments with Cumulative Effects Scenario Modeling for Grizzly Bear Connectivity in the Bow Valley, Alberta, Canada

Persistence of sensitive wildlife in populated regions requires conservation strategies that address gradual expansion of development footprint and human activity. The project-based environmental assessment regime for municipal development is poorly suited to provide necessary strategic perspective, given its focus on local and short-term impacts. We used the ALCES cumulative effects model to strategically assess impacts to grizzly bears (Ursus arctos) in the Bow Valley of Alberta, Canada. Landscape simulation mapped expansion of past and potential future development footprint in the region over multiple decades. Consequences to movement connectivity for grizzly bears were estimated by applying a least cost path analysis to the landscape simulation. An index of recreational activity was derived from fitness tracking data and integrated with the landscape simulation to model change in recreational activity through time. Maps of grizzly bear connectivity and recreational activity were combined to calculate human-bear conflict risk. The analysis suggests that connectivity has been altered through displacement to upslope areas by settlement expansion, such that surrounding natural areas have become important for grizzly bear connectivity. These areas are also popular for outdoor recreation, resulting in elevated human-bear conflict risk which can be expected to increase if development and human activity continue to expand in high connectivity areas. Conservation of wildlife in populated regions will be supported by broadening the scope of environmental assessment to address cumulative effects of development footprint and human activity over large spatial and temporal scales.

Modeling eelgrass spatial response to nutrient abatement measures in a changing climate

For many coastal areas including the Baltic Sea, ambitious nutrient abatement goals have been set to curb eutrophication, but benefits of such measures were normally not studied in light of anticipated climate change. To project the likely responses of nutrient abatement on eelgrass (Zostera marina), we coupled a species distribution model with a biogeochemical model, obtaining future water turbidity, and a wave model for predicting the future hydrodynamics in the coastal area. Using this, eelgrass distribution was modeled for different combinations of nutrient scenarios and future wind fields. We are the first to demonstrate that while under a business as usual scenario overall eelgrass area will not recover, nutrient reductions that fulfill the Helsinki Commission's Baltic Sea Action Plan (BSAP) are likely to lead to a substantial areal expansion of eelgrass coverage, primarily at the current distribution's lower depth limits, thereby overcompensating losses in shallow areas caused by a stormier climate.

Probabilistic Modeling of Exam Durations in Radiology Procedures

In this paper, we model the statistical properties of imaging exam durations using parametric probability distributions such as the Gaussian, Gamma, Weibull, lognormal, and log-logistic. We establish that in a majority of radiology procedures, the underlying distribution of exam durations is best modeled by a log-logistic distribution, while the Gaussian has the poorest fit among the candidates. Further, through illustrative examples, we show how business insights and workflow analytics can be significantly impacted by making the correct (log-logistic) versus incorrect (Gaussian) model choices.

Alternative future analysis for assessing the potential impact of climate change on urban landscape dynamics

Assessing the impact of climate change on urban landscape dynamics (ULD) is the foundation for adapting to climate change and maintaining urban landscape sustainability. This paper demonstrates an alternative future analysis by coupling a system dynamics (SD) and a cellular automata (CA) model. The potential impact of different climate change scenarios on ULD from 2009 to 2030 was simulated and evaluated in the Beijing-Tianjin-Tangshan megalopolis cluster area (BTT-MCA). The results suggested that the integrated model, which combines the advantages of the SD and CA model, has the strengths of spatial quantification and flexibility. Meanwhile, the results showed that the influence of climate change would become more severe over time. In 2030, the potential urban area affected by climate change will be 343.60-1260.66 km(2) (5.55 -20.37 % of the total urban area, projected by the no-climate-change-effect scenario). Therefore, the effects of climate change should not be neglected when designing and managing urban landscape.

Glacial lakes in the Indian Himalayas--from an area-wide glacial lake inventory to on-site and modeling based risk assessment of critical glacial lakes

Glacial lake hazards and glacial lake distributions are investigated in many glaciated regions of the world, but comparably little attention has been given to these topics in the Indian Himalayas. In this study we present a first area-wide glacial lake inventory, including a qualitative classification at 251 glacial lakes >0.01 km(2). Lakes were detected in the five states spanning the Indian Himalayas, and lake distribution pattern and lake characteristics were found to differ significantly between regions. Three glacial lakes, from different geographic and climatic regions within the Indian Himalayas were then selected for a detailed risk assessment. Lake outburst probability, potential outburst magnitudes and associated damage were evaluated on the basis of high-resolution satellite imagery, field assessments and through the use of a dynamic model. The glacial lakes analyzed in the states of Jammu and Kashmir and Himachal Pradesh were found to present moderate risks to downstream villages, whereas the lake in Sikkim severely threatens downstream locations. At the study site in Sikkim, a dam breach could trigger drainage of ca. 16×10(6)m(3) water and generate maximum lake discharge of nearly 7000 m(3) s(-). The identification of critical glacial lakes in the Indian Himalayas and the detailed risk assessments at three specific sites allow prioritizing further investigations and help in the definition of risk reduction actions.