Prediction Of Future Scenarios Research Articles

Reconstructing historical forest spatial patterns based on CA-AdaBoost-ANN model in northern Guangzhou, China

Influenced by natural and man-made factors—especially urbanization—regional forest landscapes and structures are in a dynamic process of constant change. It is of great significance to reconstruct the historical pattern of forest landscapes and construct maps of forest landscapes for long time series. Based on the investigation of Fengshui and carbon sequestration forests in northern Guangzhou city, this study combined with an artificial neural network (ANN) improved using the AdaBoost algorithm to create a cellular automaton (CA) to reconstruct species compositions and spatial distributions of historical forests. The model had the best effect at a 30 m spatial scale. At 30 m spatial scale, compared with the actual forest community’s spatial distribution, the overall accuracy of forest community distribution reconstructed by our retrospective model in 2000 was 89.17 %, the Lee Sallee index was 0.7972, and the landscape similarity index was 84.62 %. In 1990, the overall accuracy was 86.78 %, the Lee Sallee index was 0.7604, and the landscape similarity index was 80.84 %. This study provides an effective method for the reconstruction of forest vegetation patterns and the prediction of future scenarios.

Combined climate impacts and vulnerability index on coastal ecosystems in prediction of future scenarios: extended sustainable indicator tool for adaptive strategy

This study presents the coastal vulnerability due to the forecasted climate change impact on the marine environment, including the sea level rise physical trait of risk impact. A combined methodology using Representative Concentration Pathways (RCPs), which corresponds to the greenhouse gas emissions scenarios, is used in this research; combined with Climate Change Vulnerability Index (CCVI) to rank the relative risk for each of the marine ecosystem zones in relation to the potential hazard exacerbated by climate change and sea-level rise. This method presents vulnerability in numerical data, which cannot be calculated directly based on their physical properties. From the results, it shows that the coastal areas of the study area of Marudu Bay would experience a warmer atmosphere both under RCP 4.5 and RCP 8.5 with an increment of 1.0 °C and 1.7 °C; meanwhile, the climate projection for total exhibits of increase in total precipitation by 2.6 mm/day and 1.6. mm/day under RCP 4.5 and RCP 8.5 at the regional measure. At the same time, the projection simulates an increase of sea level by 0.21 m and 0.27 m over the northern region of Marudu Bay under RCP 4.5 and RCP 8.5, respectively. In addition, 43.84 ha and 57.02 ha of land estimated would be potentially inundated by the mid-century year 2050 under RCP 4.5 and RCP 8.5. By the end of the century 2100, the sea level is projected to increase locally at about 0.32 m under RCP 4.5 and 0.38 m under RCP 8.5, consequently resulting in a total of 66.84 ha and 79.78 ha of additional inundation coverage. Therefore, the result from this study can be used when making effective adaptive strategies and conservation planning despite its inherent uncertainties.

Multivariate Analysis of Drivers of Migration, Challenges, and Prediction of Future Scenarios of Female Ethiopian Return Migrants From the Middle East

The Migration of Ethiopians to Middle Eastern countries has become a common phenomenon. Research emphasizing aspects of Migration in Ethiopia has focused on the causes of Migration and the situation in the host country. But there has been no focus on future intentions of forced mass return. The internal instability of Ethiopia is also another issue for returnees staying in their homeland. Thus, this cross-sectional mixed research reveals the causes, challenges, and expectations of forced female returnee migrants in the Amhara region while they were in Migration to the Middle East, during work, and upon arrival to the homeland, as well as their future intention/plan/ after return in the homeland by applying factor analysis and binary logistic regression model. The study participants in this research were 346 forced female returnees from the Middle East selected with multistage sampling and two key informants from each study area's labor and social affairs office. Findings indicate that robbery, extortion, and lack of accommodation were challenges during Migration; verbal abuse, restricted mobility, and communication, no days off at work, and not receiving a wage for work were challenges during work in the Middle East. Besides, lack of happiness, lack of employment and support, and feeling inferiority and low self-esteem are challenges for return migrants after coming to their homeland. Most return migrants received continuous social counseling training, skill training, and entrepreneurship training. The study also reveals that currently married returnees have a significant positive intention to live in their homeland. Though, deprived economic and joblessness-related factors are statistically significant as the negative chance to live in the homeland. Lastly, the study recommends that close cooperation between all parties, governmental and non-governmental institutions, UN organizations like IOM, ILO, and other NGOs are needed to better reintegration and living of these forced returnees.

Toward Improved Model Capacities for Assessment of Climate Impacts on Coastal Bentho-Pelagic Food Webs and Ecosystem Services

Global climate change is a key driver of change in coastal waters with clear effects on biological communities and marine ecosystems. Human activities in combination with climate change exert a tremendous pressure on marine ecosystems and threaten their integrity, structure, and functioning. The protection of these ecosystems is a major target of the 14th United Nations sustainable development goal “Conserve and sustainably use the oceans, seas and marine resources for sustainable development.” However, due to the complexity of processes and interactions of stressors, the status assessment of ecosystems remains a challenge. Holistic food web models, including biological and environmental data, could provide a suitable basis to assess ecosystem health. Here, we review climate change impacts on different trophic levels of coastal ecosystems ranging from plankton to ecologically and economically important fish and shellfish species. Furthermore, we show different food web model approaches, their advantages and limitations. To effectively manage coastal ecosystems, we need both a detailed knowledge base of each trophic level and a holistic modeling approach for assessment and prediction of future scenarios on food web-scales. A new model approach with a seamless coupling of physical ocean models and food web models could provide a future tool for guiding ecosystem-based management.

Assessment of Water Supply and Demand in the Lower Mahi Sub-basin using WEAP Model

This study deals with the future water demand assessment in Lower Mahi Sub-basin, Bihar, India using Water Evaluation and Planning Model (WEAP). The river Mahi is flowing through the Mahi sub-basin, which is a tributary to the Ganga river. Understanding and analyzing the past and present water supply and demand scenarios and prediction of future scenarios are indispensable for this area. In the WEAP model development phase, two scenarios are considered: past scenario (2004-2013), present and future scenarios (2016-2021) together. The water supply nodes used are Mahi river, groundwater and canals from Gandak river. The results from the trend of supply and demand shows that the sub-basin is water deficit for most part of year except during July to September. The water deficit is also observed during Rabi season when there is no rainfall or availability of surface water is nil for the crops, especially wheat. This results in more and more usage of groundwater and hence leads to groundwater depletion. By analyzing the results of the WEAP Model for the present and future scenario, a depletion in groundwater storage at a faster rate is observed with the existing climatic conditions and increased population, livestock and industry.

Integração de ferramentas de modelagem ambiental, fragmentação da paisagem e tratamentos estatísticos na avaliação da qualidade das águas

A modelagem ambiental é um processo computacional de amplas aplicações, incluindo projeção de cenários por meio dos autômatos celulares, em que células interagem por meio de condições predefinidas, possibilitando a previsão de futuros cenários. Dessa forma é possível avaliar, por exemplo, a evolução de atividades antrópicas em bacias hidrográficas com o uso de base cartográfica e imagens de satélite. Essa projeção é importante para a qualidade das águas, visto que os recursos hídricos são amplamente prejudicados por atividades humanas. Desse modo, o presente estudo tem como objetivo simular cenários para 15 anos em uma área localizada na bacia hidrográfica do ribeirão Vermelho, que abrange os municípios de Sabará, Santa Luzia e Taquaraçu de Minas, situados na Região Metropolitana de Belo Horizonte (MG), integrando-os com a fragmentação da paisagem e dados de qualidade das águas. As diferentes informações levantadas foram associadas para se inferir sobre o estado atual das águas e predizer, conforme cenários futuros, tomadas de decisões para melhoria da sua qualidade. Inicialmente, foram mapeados em classes o uso e a ocupação do solo da bacia hidrográfica de três anos (1996, 2006 e 2016). As classes foram divididas em áreas naturais e antrópicas e associadas aos arquivos de drenagem, malha viária e altitude, sendo os inputs para o modelo de projeções de paisagem. A projeção indicou que, entre 1996 e 2016, os remanescentes de vegetação arbórea na bacia hidrográfica reduziram-se de 70 para 55%, e os resultados de análise de água mostraram que o ribeirão não atende aos valores de enquadramento conforme deliberação normativa (DN) 20/1997. Os cenários projetados pelo modelo de paisagem demonstraram o avanço das áreas antropizadas sobre as naturais, as quais em 2032 poderão se sobressair, cobrindo cerca de 60% da bacia hidrográfica. Além disso, essas projeções associadas aos parâmetros físico-químicos indicam tendência à diminuição da qualidade das águas, sendo necessárias tomadas de decisões para a melhora dessa projeção, visando atender ao enquadramento em classe I do curso de água.

Nitrogen flow patterns in the food system among cities within urban agglomeration: A case study of the Pearl River Delta region

Human activities along the entire food supply-consumption-waste treatment-recycling chain have an essential influence on Nitrogen (N) metabolic features, especially for densely-populated urban agglomeration. A few studies carried out research on detailed analysis and comparison of N flow patterns along the entire food chain among cities, to recognize these influences and accordingly explore effective measures for improving N use efficiencies. In this study, we developed an integrated N flow analysis model to quantify N flows in the food system illustrated by production, processing, consumption, and waste management sectors. Influence of anthropogenic activities on N flow patterns is recognized through comparison among cities and predictions of future scenarios. Using the Pearl River Delta (PRD) region as a case study, we find that (1) in 2016, the annual N import into the production sector in the food system in the PRD region was about 714.5 Gg, among which only 241.6 Gg entered food products. The removal rate of N pollution in all waste stream was about 62.3%, and only 9% of N became resources through reclamation. (2) Among the nine cities in the PRD region, the average amounts of N pollution emission to the air, water, and soil all range from 0.57–5.38 kg cap−1 yr−1, showing significant discrepancy among cities. Cities with relatively lower economic development undertake substantial N pollution embedded in their exported agricultural products. (3) Recycling of agricultural waste is the prior N management measure for Zhaoqing, Jiangmen, and Huizhou, while highly urbanized cities should mainly concentrate on recycling of food waste and sewage sludge. We further put forward suggestions such as cross-city resource recycling to realize better N resource recycling and pollution reduction on the whole urban agglomeration scale. This study provides an in-depth example of depicting N flow patterns and identifying proper N management measures for urban agglomerations.

NUMERICAL SIMULATION OF FLOW AND BIOKINETIC PROCESSES IN SUBSURFACE FLOW CONSTRUCTED WETLANDS: A SYSTEMATIC REVIEW

Constructed wetlands are engineered systems designed and constructed to treat wastewater using natural functions of vegetation, soils and microbial assemblages. Seeking the optimization of these systems, numerical simulation models have been developed in order to obtain parameters for design, representation of treating conditions and prediction of future scenarios. This paper reviews the state of the art on the numerical models for simulating flow and biokinetic processes in subsurface flow constructed wetlands. In this review, it is highlighted the application and performance of five software applied to simulate saturated horizontal flow (CWM1-RETRASO and PHWAT), variably saturated vertical flow (FITOVERT and HYDRUS-CW2D) and variably saturated vertical or horizontal flow (HYDRUS-CWM1). For vertical flow, FITOVERT requires less data input, but HYDRUS-CW2D simulates transformation of phosphorus and ammonia adsorption. For horizontal flow, HYDRUS-CWM1 and CWM1-RETRASO have similar approach in terms of species and number of reactions, but HYDRUS-CWM1 simulates the effect of transpiration and the limitations of bacterial growth. To simulate phosphorus in horizontal flow, the only applicable software is PHWAT, besides be able to simulate of gas production. In Brazil, a few studies address the subject of constructed wetlands simulation. Some of them apply software such as AQUASIM and MATLAB to support flow and transport equations.

Breast Cancer Care-It's Time to Rethink and Redesign

Notoriously recognized as the principal cause of mortality among women worldwide, breast cancer calls for an immediate redressal in order to devise combative strategies. Currently, Northern America and Western Europe experience more than half of the global burden of breast cancer [1]. However, as recently reported by International Agency for Research on Cancer (IARC), rapid societal and economic transitions herald an epidemiological shift in the incidence, with more low income countries projected for an increased burden of breast cancer [2]. Although the incidence rates remain higher in more developed countries, mortality and morbidity are higher in less developed regions, attributable to late detection and lack of access to advanced medical amenities. Regardless of epidemiological prevalence, the etiological determinants of breast cancer development remain common and include age, family history, genetic risk factors such as BRCA1 and BRCA2 mutations and hormonal risk factors [3]. Although viruses have been etiologically associated with many cancers, a viral etiology to breast cancer is at best speculative. There is conflicting evidence of roles of viruses such as human mammary tumor virus, human papillomavirus, Epstein-Barr virus, human cytomegalovirus, herpes simplex virus and measles virus in breast cancer development [4]. Nonetheless, a recent development that levels of mutagenic antiviral enzyme APOBEC3B are elevated in a majority of breast cancers warrants a re-visit to the role of viruses in breast cancer pathogenesis [5]. Keeping in view the current scenario, there is an urgency to assess the worldwide trends and risk factors for prediction of future scenarios with the ultimate goal of developing effective, affordable and prioritized approaches for breast cancer control.

Improving Regional Dynamic Downscaling with Multiple Linear Regression Model Using Components Principal Analysis: Precipitation over Amazon and Northeast Brazil

In the current context of climate change discussions, predictions of future scenarios of weather and climate are crucial for the generation of information of interest to the global community. Due to the atmosphere being a chaotic system, errors in predictions of future scenarios are systematically observed. Therefore, numerous techniques have been tested in order to generate more reliable predictions, and two techniques have excelled in science: dynamic downscaling, through regional models, and ensemble prediction, combining different outputs of climate models through the arithmetic average, in other words, a postprocessing of the output data species. Thus, this paper proposes a method of postprocessing outputs of regional climate models. This method consists in using the statistical tool multiple linear regression by principal components for combining different simulations obtained by dynamic downscaling with the regional climate model (RegCM4). Tests for the Amazon and Northeast region of Brazil (South America) showed that the method provided a more realistic prediction in terms of average daily rainfall for the analyzed period prescribed, after comparing with the prediction made by set through the arithmetic averages of the simulations. This method photographed the extreme events (outlier) that the prediction by averaging failed. Data from the Tropical Rainfall Measuring Mission (TRMM) were used to evaluate the method.

Threats and opportunities for post-closure development in dolomitic gold mining areas of the West Rand and Far West Rand (South Africa) – a hydraulic view Part 3: Planning and uncertainty – lessons from history

Mining is exposed to geological uncertainty as well as to economic forces beyond its control, such as commodity prices and exchange rates that govern profitability. Predictions of future scenarios in mining areas are thus inherently difficult and unreliable. This uncertainty is exacerbated by the long time periods required for pro-active planning of post-mining developments often spanning several decades. This paper presents examples from a gold mining area in the Far West Rand (South Africa) illustrating the variance between predicted scenarios and reality. The facts are embedded in a historical recount of events crucial for the design and approval of mine-closure plans, as well as post-mining development. It is argued that historical arrangements and data need to be understood and preserved in order to avoid the repetition of (costly) mistakes made in the Far West Rand. Owing to the pivotal role of water in the semi-arid area and the fact that some of the most important groundwater resources of South Africa were impacted on by deep-level mining, this paper in 3 parts adopted a largely hydraulic perspective. The loss of ‘institutional memory’ and local expertise has been identified as the main threat to planning. Part 3 presents an attempt to counteract such loss by providing an account of the events of 5 decades, as witnessed by the first author.

A simulation environment for the techno-economic performance prediction of water and power cogeneration systems using renewable and fossil energy sources

Small-scale decentralised water and power cogeneration systems with hybrid renewable and fossil power and process heat supply must not only exhibit a technically sound design, but the chosen configuration must also be economically viable. The availability of competing technical solutions to generate the commodities drinking water and electricity in combination with the uncertainty related with the prediction of future scenarios necessitates the transparent and computer-aided comparison of different options. This paper describes the basic features of the simulation environment IPSEpro and how it was adapted to allow for the specific needs of feasibility assessment studies on de-central water and power supply integrating renewable energy in MENA countries. IPSEpro is an open framework for process modelling and consists out of several modules for specific purposes. The basic modules are the Process Simulation Environment (PSE) and the Model Development Kit (MDK). They are used for building and solving process models and for defining new component models, respectively. PSEconomy is an extension module for the techno-economic analysis of processes and allows detailed evaluation of life cycle cost for different technical process configurations under varying economic scenarios. The process specific technical and economic details are contained in so-called model libraries which are created and extended with MDK. With this approach it is possible for end-users to develop very detailed but still adaptable solutions without the need to get into time-consuming high-level programming. The special model library RESYSproDESAL covers a wide range of technologies for energy conversion and sea- and brackish water desalination with emphasis on energy supply from renewable sources. Cost data are allocated to system components contained in the library as functions of size and capacity. They are based on market information, and cost parameters as well as functional relationships can be updated at any time. The economic assessment is performed through a complete through-life cycle cost analysis, which includes the total capital cost of the plant, the cost of fuel, O&M costs and the expected revenues from the sale of power and water at different operating conditions. Varying economic scenarios, including anticipated inflation and fluctuations in fuel prices and product revenues, can be taken into account. PSEconomy generates different profitability measures like net present value (NPV), internal rate of return (IRR) and product through-life costs, which can be used for economic assessment. The capabilities of the software are illustrated by an exemplary case study based on RESYSproDESAL models.