Reactive Transport Modeling for Supporting Climate Resilience at Groundwater Contamination Sites

Climate resilience is an emerging issue at contaminated sites and hazardous waste sites, since projected climate shifts (e.g., increased/decreased precipitation) and extreme events (e.g., flooding, drought) could affect ongoing remediation or closure strategies. In this study, we develop a reactive transport model (Amanzi) for radionuclides (uranium, tritium, and others) and evaluate how different scenarios under climate change will influence the contaminant plume conditions and groundwater well concentrations. We demonstrate our approach using a two-dimensional reactive transport model for the Savannah River Site F-Area, including mineral reaction and sorption processes. Different recharge scenarios are considered by perturbing the infiltration rate from the base case, as well as considering cap failure and climate projection scenarios. We also evaluate the uranium and nitrate concentration ratios between scenarios and the base case to isolate the sorption effects with changing recharge rates. The modeling results indicate that the competing effects of dilution and remobilization significantly influence pH, thus changing the sorption of uranium. At the maximum concentration on the breakthrough curve, higher aqueous uranium concentration implies that sorption is reduced with lower pH due to remobilization. To better evaluate the climate change impacts in the future, we develop the workflow to include the downscaled CMIP5 (Coupled Model Intercomparison Project) climate projection data in the reactive transport model, and evaluate how residual contamination evolves through 2100 under four climate Representative Concentration Pathway (RCP) scenarios. The integration of climate modeling data and hydro-geochemistry models enables us to quantify the climate change impacts, assess which impacts need to be planned for, and therefore assist climate resiliency efforts and help guide site management.

Performance Evaluation of CMIP6 Global Climate Models for Selecting Models for Climate Projection over Nigeria

This study assessed the performances of 13 GCMs of the CMIP6 in replicating precipitation and maximum and minimum temperatures over Nigeria during 1984–2014 in order to identify the best GCMs for multi model ensemble aggregation for climate projection. The study uses the monthly full reanalysis precipitation product Version 6 of Global Precipitation Climatology Centre and the maximum and minimum temperature CRU version TS v. 3.23 products of Climatic Research Unit as reference data. The study applied five statistical indices namely, normalized root mean square error, percentage of bias, Nash-Sutcliffe efficiency, and coefficient of determination; and volumetric efficiency. Compromise programming (CP) was then used in the aggregation of the scores of the different GCMs for the variables. Spatial assessment, probability distribution function, Taylor diagram, and mean monthly assessments were used in confirming the findings from the CP. The study revealed that CP was able to uniformly evaluate the GCMs even though there were some contradictory results in the statistical indicators. Spatial assessment of the GCMs in relation to the observed showed the highest ranked GCMs by the CP were able to better reproduce the observed properties. The least ranking GCMs were observed to have both spatially overestimated or underestimated precipitation and temperature over the study area. In combination with the other measures, the GCMs were ranked using the final scores from the CP. IPSL-CM6A-LR, NESM3, CMCC-CM2-SR5, and ACCESS-ESM1-5 were the highest ranking GCMs for precipitation. For maximum temperature, INM.CM4-8, BCC-CSM2-MR, MRI-ESM2-0, and ACCESS-ESM1-5 ranked the highest while AWI-CM-1-1-MR, IPSL-CM6A-LR, INM.CM5-0, and CanESM5 ranked the highest for minimum temperature.

The Impacts of Climate Change on the Windows of Offshore Operations

Ocean renewable energy has a central role to play in decarbonizing the global energy system. The emergence of new technologies such as floating wind farms will significantly increase offshore wind deployment by providing access to large areas of the seabed that are not suitable for fixed bottom turbines. Operations and Maintenance (O&M) is estimated to contribute 50% to an offshore wind farm’s total operational cost. The ability to improve the efficiency of O&M activities will enable offshore wind to compete with traditional fossil-based and onshore-renewable generation methods. To achieve this, an accurate characterization of the metocean environment is a mechanism of reducing delays and costs across the entire project lifecycle. One of the most significant costs associated with offshore operations is accessing a site with vessels. Site access is determined using vessels constraints in the maximum allowable meteorological and ocean (metocean) conditions and is defined as weather window analysis. However, industry guidelines and standards rely on historical data and do not consider the impact of climate change on the marine climate and the associated vessel operability. This requires the use of climate projection data. The opportunity to use an existing industry metric such as weather windows will tailor the climate projection data to the end-users needs. This paper’s findings suggest that climate change will alter the metocean environment and vessel operability for the case study location investigated. The findings demonstrate the value of site-specific assessment of the future wave climate to inform operational decision making. The main conclusion is that longer-term planning will require the offshore wind sector to consider the impact of climate change on O&M activities.

Presenting Climate Projection Ensembles as Mean and Reasonable Worst Case, with Application to EURO-CORDEX Precipitation

Users of ensemble climate projections have choices with respect to how they interpret and apply the ensemble. A simplistic approach is to consider just the ensemble mean and ignore the individual ensemble members. A more thorough approach is to consider every ensemble member, although for complex impact models this may be unfeasible. Building on previous work in ensemble weather forecasting we explore an approach in-between these two extremes, in which the ensemble is represented by the mean and a reasonable worst case. The reasonable worst case is calculated using Directional Component Analysis (DCA), which is a simple statistical method that gives a robust estimate of worst-case for a given linear metric of impact, and which has various advantages relative to alternative definitions of worst-case. We present new mathematical results that clarify the interpretation of DCA and we illustrate DCA with an extensive set of synthetic examples. We then apply the mean and worst-case method based on DCA to EURO-CORDEX projections of future precipitation in Europe, with two different impact metrics. We conclude that the mean and worst-case method based on DCA is suitable for climate projection users who wish to explore the implications of the uncertainty around the ensemble mean without having to calculate the impacts of every ensemble member.