Regional Impact Assessment Research Articles

Climate trend analysis in the ramis catchment, upper wabi shebelle basin, Ethiopia, using the CMIP6 dataset

Climate change, a global concern, has significant implications for rainfall and temperature patterns. A critical knowledge gap exists in understanding these implications for the Ramis catchment of the Upper Wabi-Shebelle Basin. This research projects future rainfall and temperature patterns in the Ramis catchment of the Upper Wabe-Shebelle basin. The projection utilized a multimodel comprising eight distinct general circulation models from coupled model intercomparison phase six (CMIP6) data simulation. These models were selected based on their performance, considering two shared socioeconomic pathways (SSP2-4.5 and SSP5-8.5). The Quantile Mapping (QM) bias correction technique, implemented in R, was used to enhance the reliability of extracted data. The Mann‒Kendall (MK) trend test method was employed to analyze temperature and precipitation trends for two future periods, 2030–2060 and 2061–2090, annually and seasonally. The results suggest a decrease in rainfall during the spring and Winter seasons for 2030 to 2060 and 2061 to 2090 respectively, potentially leading to water scarcity that could impact crop growth and water supply. A decreasing trend in annual rainfall was observed from 2030 to 2060 under SSP2-4.5 and SSP5-8.5 scenarios. The study also uncovered significant and consistent warming trends in maximum and minimum temperatures across the Ramis catchment under both the SSP2-4.5 and SSP5-8.5 scenarios. Rainfall Anomaly Index (RAI) analysis predicts a minor increase in rainfall in the Ramis catchment from 2030 to 2090 under both scenarios, with a notable RAI decrease expected in 2043–2071, indicating potential drought conditions. These findings offer critical insights for regional climate change impact assessments across the Ramis catchment.

Toward a common categorization for valued components: using a review of valued components and indicators in the lower James Bay Region of Ontario and Quebec, Canada, to support cumulative impact science in Canada

ABSTRACT Cumulative impacts and regional assessments (RA) require the integration and analysis of large quantities of interdisciplinary scientific information. Yet, the information available is not often readily accessible nor standardized across impact assessments. We proposed here a categorization of valued components that have been derived from projects in the James Bay Lowlands and that is aligned with the IAAC’s Tailored Impact Statement Guidelines (TISG) template, a key document to support project and regional impact assessment deliverables. We compiled valued components and indicators from previous and ongoing environmental impact assessments in the James Bay Lowlands. We identified trends associated with valued components and indicators and combined these trends with the TISG. We then derived a categorization of valued components nested under seven systems. From the reviewed work, we identified 197 valued components (23% valued components were common for two or more projects) and 313 indicators. Our categorization is composed of seven systems and 34 potential valued components allowing for easy connections between future project-level impact assessments and RAs. Our categorization is a valuable communication tool across all stakeholders and rightsholders involved in impact assessments, including communities, Indigenous leaders, IA practitioners, industry, and government.

Is Bias Correction in Dynamical Downscaling Defensible?

AbstractLocalized projections of 21st‐century hydroclimate variables obtained from downscaling Global Climate Model (GCM) output are central to informing regional impact assessments and infrastructure planning. Regional GCM biases can be significant and, for dynamical downscaling, can be addressed either before (a priori) or after (a posteriori) downscaling. However, a priori bias correction (APBC) has generally unexplored effects on climate change signals. Here we analyze dynamically downscaled solutions of CMIP6 GCMs over the Western U.S., with and without APBC, and quantify APBC's impact on climate change signals relative to other irreducible uncertainty sources. For temperature and precipitation, the uncertainty introduced by APBC is negligible compared to that arising from GCM choice or internal variability. Furthermore, APBC greatly reduces regional models' unrealistically high snow‐water‐equivalent (SWE) biases that result directly from GCM errors. We leverage this finding to encourage the dynamical downscaling community to adopt APBC as a standard operating procedure.

Sub-grid Variability and its Impact on Exposure in Regional Scale Air Quality and Integrated Assessment Models: Application of the uEMEP Downscaling Model

Regional scale air quality and integrated assessment models are necessarily limited in their spatial resolution, particularly when applied to larger regions such as Europe or for global applications. The EMEP MSC-W chemical transport model and the GAINS integrated assessment model, which makes use of source receptor information precalculated by the EMEP MSC-W model, use resolutions of 0.1°×0.1° and 0.3°×0.2°. These resolutions cannot account for variability at finer scales. Variability within grids, both concentration and population distributions, can be significant. To improve exposure calculations that take into account the sub-grid variability, the uEMEP model has been applied in this study to provide suitable parameterisations of the sub-grid variability within an EMEP MSC-W grid. The uEMEP model is applied on a European domain at 250 × 250 m2. This provides roughly 7000 sub-grids within each EMEP MSC-W 0.3°×0.2° grid to analyse the sub-grid variability in exposure. The analysis derives an exposure correction factor (ECF) for each source sector in each EMEP MSC-W grid. This factor provides information on the impact of sub-grid resolution on the population weighted concentrations within each grid. The population weighted standard deviation is also calculated, providing information on sub-grid variability to derive frequency distributions and parameterised forms of the sub-grid variability suitable for health risk assessments. The impact of sub-grid variability is assessed for the pollutants PM2.5, NOX and NO2. For NO2, additional chemistry calculations are carried out on the NOX frequency distributions. We show that the parameterised form of the sub-grid variability provides close to equivalent results as the original uEMEP calculations. Exposure to PM2.5 increases by around 13% when the sub-grid variability is taken into account for a 2030 emission scenario. For NO2, this increase is around 31% for the same scenario. A similar increase is found in the health risk indicator ’population attributable fraction’ when no lower concentration threshold is used in the concentration–response function. When applying the World Health Organizations recommended threshold value we find that including the sub-grid variability increases the calculated population attributable fraction by 32% and 46% for PM2.5 and NO2 respectively. We conclude that sub-grid variability should be taken into account in future exposure calculations for regional scale modelling and impact assessments.

Functional or Neglected Border Regions? Analysis of the Integrated Development Plans of Borderland Municipalities in South Africa

The mainstream approach of regional integration impact assessments is mainly limited to assessing cross-border development projects/programmes. There is still a lack of critical assessment of how stakeholders at different institutional levels conceptualise the border. Local (municipal) strategic plans provide a reflection of the spatial imaginaries of stakeholders, perception planners, institutional power structures, and, to some extent meaning of the border to the local people. Integrated Development Plans (IDPs) in South Africa were adopted as an important development planning strategy in the post-apartheid era. IDPs of 49 borderland municipalities were systematically reviewed using the Key-Word-in-Context (KWIC) content analysis technique of the keyword ‘border’ to determine the importance of state borders in light of regional integration. Border security and management is one of the most common themes associated with the border. This suggested that borders were mainly perceived as threats and barely considered as a potential resource for cross-border cooperation or integration.

Regional impact assessment of air quality improvement: The air quality lifecourse assessment tool (AQ-LAT) for the West Midlands combined authority (WMCA) area

Poor air quality is the largest environmental health risk in England. In the West Midlands, UK, ∼2.9 million people are affected by air pollution with an average loss in life expectancy of up to 6 months. The 2021 Environment Act established a legal framework for local authorities in England to develop regional air quality plans, generating a policy need for predictive environmental impact assessment tools. In this context, we developed a novel Air Quality Lifecourse Assessment Tool (AQ-LAT) to estimate electoral ward-level impacts of PM2.5 and NO2 exposure on outcomes of interest to local authorities, namely morbidity (asthma, coronary heart disease (CHD), stroke, lung cancer), mortality, and associated healthcare costs. We apply the Tool to assess the health economic burden of air pollutant exposure and estimate benefits that would be generated by meeting WHO 2021 Global Air Quality Guidelines (AQGs) (annual average concentrations) for NO2 (10 μg/m3) and PM2.5 (5 μg/m3) in the West Midlands Combined Authority Area.All West Midlands residents live in areas which exceed WHO AQGs, with 2070 deaths, 2070 asthma diagnoses, 770 CHD diagnoses, 170 lung cancers and 650 strokes attributable to air pollution exposure annually. Reducing PM2.5 and NO2 concentrations to WHO AQGs would save 10,700 lives reducing regional mortality by 1.8%, gaining 92,000 quality-adjusted life years (QALYs), and preventing 20,500 asthma, 7400 CHD, 1400 lung cancer, and 5700 stroke diagnoses, with economic benefits of £3.2 billion over 20 years. Significantly, we estimate 30% of QALY gains relate to reduced disease burden. The AQ-LAT has major potential to be replicated across local authorities in England and applied to inform regional investment decisions.

Land Resources for Wind Energy Development Requires Regionalized Characterizations.

Estimates of the land area occupied by wind energy differ by orders of magnitude due to data scarcity and inconsistent methodology. We developed a method that combines machine learning-based imagery analysis and geographic information systems and examined the land area of 318 wind farms (15,871 turbines) in the U.S. portion of the Western Interconnection. We found that prior land use and human modification in the project area are critical for land-use efficiency and land transformation of wind projects. Projects developed in areas with little human modification have a land-use efficiency of 63.8 ± 8.9 W/m2 (mean ±95% confidence interval) and a land transformation of 0.24 ± 0.07 m2/MWh, while values for projects in areas with high human modification are 447 ± 49.4 W/m2 and 0.05 ± 0.01 m2/MWh, respectively. We show that land resources for wind can be quantified consistently with our replicable method, a method that obviates >99% of the workload using machine learning. To quantify the peripheral impact of a turbine, buffered geometry can be used as a proxy for measuring land resources and metrics when a large enough impact radius is assumed (e.g., >4 times the rotor diameter). Our analysis provides a necessary first step toward regionalized impact assessment and improved comparisons of energy alternatives.

The suitability of differentiable, physics-informed machine learning hydrologic models for ungauged regions and climate change impact assessment

Abstract. As a genre of physics-informed machine learning, differentiable process-based hydrologic models (abbreviated as δ or delta models) with regionalized deep-network-based parameterization pipelines were recently shown to provide daily streamflow prediction performance closely approaching that of state-of-the-art long short-term memory (LSTM) deep networks. Meanwhile, δ models provide a full suite of diagnostic physical variables and guaranteed mass conservation. Here, we ran experiments to test (1) their ability to extrapolate to regions far from streamflow gauges and (2) their ability to make credible predictions of long-term (decadal-scale) change trends. We evaluated the models based on daily hydrograph metrics (Nash–Sutcliffe model efficiency coefficient, etc.) and predicted decadal streamflow trends. For prediction in ungauged basins (PUB; randomly sampled ungauged basins representing spatial interpolation), δ models either approached or surpassed the performance of LSTM in daily hydrograph metrics, depending on the meteorological forcing data used. They presented a comparable trend performance to LSTM for annual mean flow and high flow but worse trends for low flow. For prediction in ungauged regions (PUR; regional holdout test representing spatial extrapolation in a highly data-sparse scenario), δ models surpassed LSTM in daily hydrograph metrics, and their advantages in mean and high flow trends became prominent. In addition, an untrained variable, evapotranspiration, retained good seasonality even for extrapolated cases. The δ models' deep-network-based parameterization pipeline produced parameter fields that maintain remarkably stable spatial patterns even in highly data-scarce scenarios, which explains their robustness. Combined with their interpretability and ability to assimilate multi-source observations, the δ models are strong candidates for regional and global-scale hydrologic simulations and climate change impact assessment.

Editorial: Climate change information for Regional impact and risk assessment

Editorial: Climate change information for Regional impact and risk assessment

EESD special issue: AI and data‐driven methods in earthquake engineering – (Part 1)

EESD special issue: AI and data‐driven methods in earthquake engineering – (Part 1)

A regional scale impact and uncertainty assessment of climate change in the Western Ghats in India.

The general circulation models (GCMs) and emission scenarios (RCP 4.5 and 8.5) have proven to be significantly functional in evaluating the impacts of climate change (CC) on hydrology, although their performance and accuracy varies on a regional scale. The objective of the present study is to evaluate the performance of five CMIP5 GCMs (CanESM2, BNU-ESM, CNRM-CM5, MPI-ESM-LR and MPI-ESM-MR) on a regional scale in the West Flowing River Basins-2 (WFRB-2) in India to model the impact of CC and its scenario uncertainty using reliability ensemble average (REA) method. For quantifying the results, the upper, middle and lower regions of WFRB-2 are separately analysed. The MPIMR and MPILR GCM model shows highest reliability factor range (0.3-0.6) in predicting the annual mean and annual maximum rainfall for most of the grids in the region. The GCM-simulated runoff using VIC (variable infiltration capacity) model is evaluated using statistical parameters such as root mean square error (RMSE), percentage bias (Pbias) and standard deviation (Std). The annual mean (maximum) runoff obtained using REA ensemble shows least RMSE, Pbias and Std values, i.e. 21.08%, 9.10mm and 8.9mm (6%, 39.1mm, 39.1mm), respectively for the middle region, which demonstrates higher reliability of GCM outputs in the flood-prone regions of WFRB-2. Furthermore, the future projection of annual maximum rainfall/runoff shows an increase of 50mm/15mm in the near future (2011-2040) for lower and 20mm/6mm for middle regions, which may cause flooding activities in the lower and middle region of WFRB-2.

Activity concentrations and radiological hazard assessments of 226Ra, 232Th, 40K, and 137Cs in soil samples obtained from the Dongnam Institute of Radiological & Medical Science, Korea

The radioactivity concentration of environmental radionuclides was analyzed for soil and sand at eight locations within a radius of 255 m centered on the Dongnam Institute of Radiological & Medical Science (DIRAMS), Korea. The average activity concentrations of 40K, 137Cs, 226Ra, and 232Th were 661.1 Bq/kg-dry, 0.9 Bq/kg-dry, 21.9 Bq/kg-dry, and 11.1 Bq/kg-dry, respectively. The activity of 40K and 137Cs was lower than the 3-year (2017–2019) average reported by the Korea Institute of Nuclear Safety, respectively. Due to the nature of granite-rich soil, the radioactivity of 40K was 0.6-fold higher than in other countries, while 137Cs was in the normal fluctuation range (15–30 Bq/kg-dry) of the concentration of radioactive fallout from nuclear tests. The activity of 226Ra and 232Th was lower than in Korean soils reported by the United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR). The average activity concentrations of 232Th and 40K for the soil and sand samples from DIRAMS were within the range specified by UNSCEAR in 2000. The radium equivalent activity and internal and external hazard index values were below the recommended limits (1 mSv/y). These radionuclide concentration (226Ra, 232Th, 40K, and 137Cs) data can be used for regional environmental monitoring and ecological impact assessments of nuclear power plant accidents.

A temporally dynamic model for regional carbon impact assessment based on city information modeling

Cities consume a large amount of energy and discharge massive greenhouse gas emissions, which are key areas supporting low-carbon development and combating the global warming crisis. Conducting regional impact assessments has been a trending research topic, but not without its challenges; especially, foreground data acquisition and temporal dynamics consideration. This study integrates City Information Modeling (CIM) and Dynamic Life Cycle Assessment (DLCA) to develop a new regional carbon impact assessment model, including five parts: goal and scope definition, CIM module, foreground elementary flows analysis, dynamic assessment, and interpretation. In the integration model, CIM is used to extract the geometric and semantic data of the physical elements and geospatial information of built environment. Temporal variations in foreground elementary flows, background inventory datasets, characterization factors, and weighting factors were quantified and involved. A university campus was used to verify the CIM-DLCA integration model, and the impact results were visualized and analyzed from temporal and spatial dynamic perspectives. The influences and sensitivity of dynamic factors were quantified and compared. In addition, the uncertainty issues, data acquisition advantages, and CIM-DLCA integration strategies were discussed. Some valuable future research directions were proposed. This study takes an exploratory step and demonstrates that combining CIM with DLCA is both feasible and operable. This provides a theoretical foundation for intelligent assessment and can be used to promote low-carbon city management.

COVID-19 and the role of Voluntary, Community, and Social Enterprises in northern England in responding to the needs of marginalised communities: a qualitative focus group study

BackgroundThe Voluntary Community and Social Enterprise sector has a crucial role in supporting the health and wellbeing of people who are marginalised or who have multiple complex needs. We aimed to understand perceptions of those working in the sector and examine the short-term, medium-term, and long-term effects of COVID-19 on Voluntary Community and Social Enterprise organisations in northern England as they respond to the needs of marginalised communities. This research formed one component of a regional multiagency Health Inequalities Impact Assessment. MethodsWe conducted qualitative focus groups with staff and volunteers from five organisations between March and July, 2021, via a video conferencing platform. Eight of nine focus groups were audio-recorded and transcribed verbatim. One focus group was not recorded due to concerns raised over anonymity and safeguarding, but non-ascribed fieldnotes were taken. Focus group transcripts were analysed using framework analysis. FindingsOne organisation supported children and young people; two organisations supported vulnerable women, young people, and families; one organisation supported refugees and asylum seekers, and one organisation supported disadvantaged individuals to improve their mental and physical health and wellbeing. Three central themes were identified: the exacerbation of pre-existing inequalities, adversity, and challenges for vulnerable and marginalised populations; the cost of being flexible, innovative, and agile for Voluntary Community and Social Enterprise staff and volunteers; and the voluntary sector as a lifeline (organisational pride and resilience). InterpretationThe considerable expertise, capacity, and resilience of Voluntary Community and Social Enterprise organisations and the crucial role they have in supporting marginalised communities has been clearly shown in their response to the COVID-19 pandemic. The Voluntary Community and Social Enterprise sector therefore has an essential role in the post-COVID levelling-up agenda. The implications of these findings for service provision are that the Voluntary Community and Social Enterprise sector must be recognised as an integral partner within any effectively functioning local health system and, as such, adequately resourced to safeguard sustainability and to ensure that attempts to involve the sector in addressing the social determinants of health are not jeopardised. FundingNational Institute for Health and Care Research (Applied Research Collaboration North East and North Cumbria (grant reference NIHR200173) and Public Health England. SSo is supported by a Health Education England and National Institute for Health and Care Research Integrated Clinical Academic Lecturer award (reference CA-CL-2018-04-ST2-010) and Research Capability Funding, National Health Service North of England Care System Support. VJM is funded by the National Institute for Health and Care Research School for Public Health Research (grant reference PD-SPH-2015).

Productive interactions in digital training partnerships: Lessons learned for regional development and university societal impact assessment

In development, social, and management theories, universities have gained increasing relevance as engines of growth and innovation. Alongside companies, universities engage in a collaborative exploration for a shared knowledge base that does not pursue immediate exploitation. Drawing on research evaluation and the regional and entrepreneurial studies on University-Industry Collaborations (UICs) and knowledge ecosystems, this article assesses the digital training partnerships the University of Naples in Italy has established with some global technology and advanced manufacturing companies. By examining their innovative performance, the case study evaluation explores university-business ‘productive interactions,’ highlighting such critical issues as the management and risks of co-innovation, the attraction of talents, and the development of human capital in a peripheral and moderate innovative region. The article critically discusses the regional development outcomes and the evaluation designs to assess the societal impact of university research and education.

Observed Changes in Climate Conditions and Weather-Related Risks in Fruit and Grape Production in Serbia

Climate change, through changes in temperature, precipitation, and frequency of extreme events, has influenced agricultural production and food security over the past several decades. In order to assess climate and weather-related risks to fruit and grape production in Serbia, changes in bioclimatic indices and frequency of the occurrence of unfavourable weather events are spatially analysed for the past two decades (1998–2017) and the standard climatological period 1961–1990. Between the two periods, the Winkler and Huglin indices changed into a warmer category in most of the viticultural regions of Serbia. The average change shift was about 200 m towards higher elevations. Regarding the frequency of spring frost, high summer temperatures and water deficit, the most vulnerable regions in terms of fruit and grape production are found alongside large rivers (Danube, Sava, Great and South Morava), as well as in the northern part of the country. Regions below 300 m are under increased risk of high summer temperatures, as the number and duration of occurrences increased significantly over the studied periods. The high-resolution spatial analysis presented here gives an assessment of the climate change influence on the fruit and grapes production. The presented approach may be used in regional impact assessments and national planning of adaptation measures, and it may help increase resilience of agricultural production to climate change.

Regional Impact Assessment of Trans-Sectoral Planning of Waste Flows in Da Nang, Vietnam

Regional Impact Assessment of Trans-Sectoral Planning of Waste Flows in Da Nang, Vietnam

Bias Correction of Climate Model Outputs Using IMD Srinagar Data for Climate Change Impact Assessment

Regional climate models (RCMs) give more credible findings for a regional climate change impact assessment, but they still have a bias that must be rectified. Two correction functions using two methods, the modified difference approach and linear scaling method, were utilized for local bias correction of Tmax, Tmin, and precipitation data at monthly scales and validated to minimize the bias between modelled (HAD GEM2-ES-GCM) and observed climate data at IMD Srinagar Station, J&K. Linear scaling technique at monthly time scale for Tmax, Tmin, and precipitation was superior to modified difference approach for bias correction of modelled data to close it to observed data.

OECD Case Studies of Integrated Regional and Strategic Impact Assessment: What Does ‘Integration’ Look Like in Practice?

Increasingly, protocols for assessing the impacts of land-uses and major resource development projects focus not only on environmental impacts, but also social and human health impacts. Regional and Strategic Environmental Assessment (RSEAs) are one innovation that hold promise at better integrating these diverse land-use values into planning, assessment, and decision-making. In this contribution, a realist review methodology is utilized to identify case studies of “integrated RSEA”—those which are strategic, have a regional assessment approach, and seek to integrate environmental, community and health impacts into a singular assessment architecture. The results of a systematic literature review are described and six RSEA-like case studies are identified: Kimberly Browse LNG SEA; HS2 Appraisal of Sustainability; Lisbon International Airport SEA; Beaufort Regional Environmental Assessment; Nordstream 2 Transboundary EIA; and the Portland Harbour Sustainability Project. The case studies are examined according to their unique contexts, mechanisms and outcomes of their assessment protocols to determine the degree to which they consider more than environmental valued components, and the means by which they were included. Findings suggest that RSEA has a contentious relationship with the integration of more than environmental values, but that there are significant lessons to be learned to support project planning, especially for assessment contexts characterized by large, transboundary projects.

Long-Term Maximum and Minimum Temperature Projections Over Metro Vancouver, Canada

Evident climate change has been observed and projected in observation records and General Circulation Models (GCMs), respectively. This change is expected to reshape current seasonal variability; the degree varies between regions. High-resolution climate projections are thereby necessary to support further regional impact assessment. In this study, a gated recurrent unit-based recurrent neural network statistical downscaling model is developed to project future temperature change (both daily maximum temperature and minimum temperature) over Metro Vancouver, Canada. Three indexes (i.e., coefficient of determinant, root mean square error, and correlation coefficient) are estimated for model validation, indicating the developed model’s competitive ability to simulate the regional climatology of Metro Vancouver. Monthly comparisons between simulation and observation also highlight the effectiveness of the proposed downscaling method. The projected results (under one model set-up, WRF-MPI-ESM-LR, RCP 8.5) show that both maximum and minimum temperature will consistently increase between 2,035 and 2,100 over the 12 selected meteorological stations. By the end of this century, the daily maximum temperature and minimum temperature are expected to increase by an average of 2.91°C and 2.98°C. Nevertheless, with trivial increases in summer and significant rises in winter and spring, the seasonal variability will be reduced substantially, which indicates less energy requirement over Metro Vancouver. This is quite favorable for Metro Vancouver to switch from fossil fuel-based energy sources to renewable and clean forms of energy. Further, the cold extremes’ frequency of minimum temperature will be reduced as expected; however, despite evident warming trend, the hot extremes of maximum temperature will become less frequent.