Regional Analysis

Modeling Impacts of Wildfires on Soil Thermal and Hydrological and Carbon Dynamics in Northern High Latitudes

Abstract: Wildfires significantly affect vegetation, soil thermal and hydrological as well as carbon dynamics. This study uses a process-based biogeochemistry modeling framework that is incorporated with land surface energy balance, soil thermal and hydrological dynamics and their effects on carbon and nitrogen cycling to simulate these dynamics and carbon budget in northern high latitudes. Here we present our model results on North American boreal forests from 1986 to 2020 using satellite-derived burn severity data. We find that fires remove ecosystem carbon through combustion emissions and reduce net ecosystem production, making the ecosystem lose 3.5 Pg C during 1986-2020 and changing the boreal forests from a carbon sink to a source in the region. Our modeling also suggests that fire-impacted canopy influences surface energy balance, inducing significant summer soil temperature changes, affecting nitrogen mineralization rate and plant nitrogen uptake, thereby changing plant net primary productivity; the altered soil temperature also affects soil carbon decomposition. As a result, the canopy effects on surface energy balance significantly affect boreal forest ecosystem carbon sink and source activities in the region. Currently we are examining the wildfire impacts on permafrost dynamics and hydrological cycle as well as carbon and nitrogen dynamics in northern Eurasia.

Practical Steps for Achieving Equity in Water Resources System Planning: Lesotho Irrigation Investment under Climate Change

Abstract: In recent years, researchers have begun to propose methods to assess distributive justice under climate change and within adaptation policies. Literature calls for a systematic approach for incorporating distributive justice in water resources planning, and clear guidelines on how best to include such an approach in standard project development and decision-making frameworks. So far, there are inadequate illustrative examples of how this is done in practice, and little connection has been made to financial evaluation and decision metrics commonly used by stakeholders. While stakeholders and decision makers rely on the outcome of financial assessment methods such as Cost Benefit Analysis (CBA) to make informed decisions on a project’s economic ‘viability’ and justify cost investments, matters of equity are often neglected. Given the complexities of equitable resource planning in the presence of uncertainties like climate change, the study presents a step-by-step methodology to robust water project investment under climate change uncertainty while exploring benefit streams from equitable spatial distribution. Making this connection between equity in the distribution of water resources and their corresponding climate risks, and financial metrics used for decision making, would prove valuable to stakeholders in both academia and industry. Further, by providing decision makers with both equity and economic metrics upon which to base their decisions, confident decisions justifying cost investment and safeguarding equitable resource distribution can be made amid changing climate conditions.

Incorporating Below Ground City Textures into Urban Flood Modeling for Enhanced Flood Prediction and Risk Assessment: A Case Study of the MIT CAMPUS

Abstract

Pluvial (rain-driven) flooding poses a significant threat to urban areas worldwide, necessitating accurate flood prediction for effective flood risk adaptation and damage mitigation. This research explores the impact of incorporating below ground city textures, such as basements, garages, and tunnels, into urban flood models on the extent and propagation of surface and subsurface flooding and damages. A high-resolution 1&2D Rain On Mesh (ROM) hydrodynamic flood model for the city of Cambridge was developed. The model integrated various layers of geospatial data and the city's DEM. An extended version of the model, the Basement Model, applies an approach to incorporate below ground city textures within a surface flooding model and was developed for the MIT campus neighborhood, approximately 10% of the case study area. The Campus has a unique feature that 33 of the buildings of the main campus are connected via their basements. A recent crowd-sources activity revealed that there are over 1000 potential points where water can enter the buildings.

Our simulation results showed significant basement flooding that altered the spatial-temporal pattern of surface flooding in the study area as compared to the surface model. The basement model identified significant water inflow to the basement reducing the volume of surface flood waters and reducing much of the surface flooding observed in the surface model. Moreover, this model detected flooding within buildings that lacked directed surface flooding due to flow between the basements.

These models were compared in a flood risk study. Using the basement model the expected damages from the 100-year flood were 33% of the expected damages using state of the art USACE surface flood damage methods. Additionally, there was significant differenced in which buildings were at risk, the total amount of flooding volume, the spatial-temporal propagation, and the damage assessments within structures.

The incorporation of basements and below ground city textures into flood modeling proves to be invaluable in accurately predicting flood extent and propagation. These findings contributed to MIT’s Climate Resilience Pathway to develop of a resilient flood management plan incorporating climate change into campus design processes.

Plain-language Summary

Incorporating the flood storage capacities of basement in urban flood modeling can have a significant impact on the potential flood damage in at building and neighborhood scale. A case study of the MIT Campus shows expected damages can be over 70% less if basements are modeled.

Urban Flood Modeling: The Influence of surface run-off modeling and Geospecificity in estimating Flood Extent and Propagation

Abstract

This research investigates the critical factors governing the extent and propagation of urban flooding, and their risks on urban systems. A drainage-catchment-based -2D (Catch) and a 1&2D Rain on Mesh (ROM) FE hydrodynamic flood models were constructed, validated, and compared for case studies of Cambridge and Cleveland. The Catch model directs the volume of run-off in a catchment directly into the pipe system bypassing any localized surface flooding in the catchment. Flooding would occur only when a pipe surcharges and that volume is routed over the surface. In contrast the ROM model distributes the rainfall directly on a 2D mesh, calculates the infiltration at each cell and utilizes the city terrain, road, green-zones and building textures geospatial data to route the surface flow via the shallow flow equations to manholes. The ROM model has coupled 1D pipe inflow/outflow with the surface 2D flow and is able to account for the interaction of surcharge flows as well as surface rainfall flows.

Spatial results of the ROM simulations, see Figure 1, revealed for a range of design storms flood extents 7-9.5 times greater and depths 1.2-7 times deeper across the city during peak flooding, as compared to the Catch model. Additionally, the ROM model effectively identified surface flooding in areas where drainage-based model predicted no flooding. The temporal flood propagation was vastly different between the two models, with the ROM model showing peak flooding that more than 5% of the city remained flooded at the conclusion as compared to the 0% for the drainage-based model. The underestimation of duration and extent of flooding greatly impacts the expected damages on urban infrastructure and current drainage-based models dramatically underestimate this simulation output. Our findings demonstrate that ROM mechanisms, when introduced in pluvial flooding simulations, along with the granular details of city texture incorporated within the model, are critical determinants of the extent and propagation of highly dynamic urban flooding. The results highlight the significance of incorporating ROM approaches and granular city information for more informed and refined pluvial and fluvial regional flood modeling, offering valuable insights into urban flooding impacts.

Plain-language Summary

Catchment-based surface runoff models underestimate the duration and extent of flooding as compared to models that incorporate of rain-on-mesh surface runoff modeling in urban drainage analysis. This underestimation of flood extent and duration leads to underestimation of the expected damages and impacts on lifespan of urban infrastructure. Case studies of Cambridge, Massachusetts and Cleveland, Ohio are presented.

Maximizing Air Quality Benefits of Organizational Climate Action: Evidence from Electricity Purchase and Personnel Business Travel

Abstract: Organizational decisions to mitigate climate change are often focused solely on reducing greenhouse gas emissions, but also can have multiple sustainability-related impacts. A substantial area of impact from reducing greenhouse gases relates to air quality, where reductions in fossil fuel use can cause health damages locally and regionally. While much research has quantified the air quality benefits of large-scale strategies to reduce greenhouse gas emissions, information about the different impacts of organizational Scope 1–3 emissions on air quality is lacking. We use data from two universities and one multinational corporation based in the northeast U.S. to examine the magnitude and location of air quality changes associated with reducing carbon emissions under two different strategies: replacing purchased fossil-based electricity with renewable energy (scope 2), and reducing personnel business travel by air (scope 3). We estimate the marginal climate response and spatially-resolved air quality impacts associated with these two strategies. To do this, we first use an energy system model (US Energy Grid Optimization, US-EGO) to simulate electricity grid responses and related emissions (CO2, NOx and SO2) due to organizational electricity consumption. We calculate business travel emissions (principally NOx, SOx and non-volatile particulate matter) with the Aviation Emission Inventory Code (AEIC) based on detailed flight data provided by the organizations. For both sectors, we run GEOS-Chem High Performance (GCHP), an atmospheric chemistry-transport model, to simulate the ground-level concentration of ozone and fine particulate matter (PM2.5). We estimate the health damages from these two pollutants with Concentration Response Functions (CRFs). We calculate the social costs using the Social Cost of Carbon (SCC) for carbon emissions, and use a Value of Statistical Life (VSL) to quantify costs for air-pollution-induced health damages. We explore how marginal estimates of damages vary depending on system-level assumptions. Finally, we compare our estimates from detailed modeling with results from reduced form air quality estimators (InMAP, AP2 and EASIUR) to identify how these tools might assist organizations in prioritizing emissions reductions to maximize overall air quality benefits.

Disentangling the Changing Nature of Water Quality, Availability, and Equity in the United States

Abstract: Threats to future water “security” are increasingly assessed through not just the lens of water and water quality, but how these may unequally expand across sociodemographic and ethnic landscapes. Evidence to date indicates that low-carbon, climate mitigation policies and targets provide marginal benefits to water scarcity trends. Therefore, effective measures require integrated solutions to co-evolving system-wide features of supply, demand, nutrient loading, and conveyance, and avoid inequities and unjust transitions. Based on our current assessment with the MIT System for the Triage of Risks from Environmental and Socioeconomic Stressors (STRESS) platform, we find co-existing areas of water stress, water quality, poverty, and minority populations are extensive – particularly in the south and southeast United States – but with important granular hotspots in populated areas. Therefore, an underlying question and scientific challenge is to understand and quantify the extent that natural and human-forced drivers affect (or benefit) these landscapes – and what are the salient response patterns amidst climatic and human-forced uncertainties?

In view of these considerations, we have conducted a suite of simulations with a linked model system that resolves the contiguous United States at over 2,100 basins and includes a water management module as well as a parsimonious water-quality model. The experimental simulations combine altered landscapes of water supply, demand, nutrient loadings, and conveyance landscapes sequentially and successively. These altered landscapes reflect plausible changes in human-forced climate patterns, land use and management (cultivated for food, agriculture, and bioenergy), water demands (domestic, industrial, energy, and agriculture), as well as water-system efficiencies. Overall, we find that uniform and large-scale patterns of these drivers produce heterogenous and complex responses across U.S. basins, but with important exceptions. These heterogenous response features, however, can be ascribed to precursory conditions of the basins’ environments, and thus indicate potentially predictable consequences. We demonstrate these predictable features through a series of future scenarios generated by our multi-sector dynamical prediction framework.

Future Patterns of US Agricultural Land Use under Multiple Stressors

Abstract: Climate change, income and population growth, and changing diets are major stressors for global agricultural markets with implications for land use change. US land use at regional and local scales is directly affected by domestic forces and indirectly through international trade. In order to investigate the effects of several potential forces on land use changes in the US at multiple spatial scales, we advanced the capabilities in representing the interactions between natural and human system through a collaborative effort between two MSD teams. This effort couples a multi-sectoral and multi-regional socio-economic model of the world economy with detailed representation of land use and agricultural systems to an open-source downscaling model which enables translating regional projections of future land use into high-resolution representations of time-evolving land cover. We exemplify the framework over the Mississippi river basin and consider the effects of a range of global drivers and stressors, such as: high or low economic and population growth, more negative or more positive impacts of climate change, and more or less dietary change. The resulting regional land use changes are further translated into more detailed projections of land use changes through the downscaling model. In addition, we examine assumptions, including 1) how global stressors might, in combination, affect regional land use change and 2) how alternative rules and constraints spatialize the regional projections. Our results help better understand the implications of land use change on carbon storage, soil erosion, chemical use, hydrology, and water quality. The employed downscaling model facilitates interoperability among models and across various spatial scales. The presented framework can be readily applied to other basins with little effort.

Logs or permits? Forestry land use decisions in an emissions trading scheme

Abstract: Negative carbon emissions options are required to meet long-term climate goals in many countries. One way to incentivize these options is by paying farmers for carbon sequestered by forests through an emissions trading scheme (ETS). New Zealand has a comprehensive ETS, which includes incentives for farmers to plant permanent exotic forests.

This research uses an economy-wide model, a forestry model and land use change functions to measure the expected proportion of farmers with trees at harvesting age that will change land use from production to permanent forests in New Zealand from 2014 to 2050. We also estimate the impacts on carbon sequestration, the carbon price, gross emissions, GDP and welfare.

When there is forestry land use change, the results indicate that the responsiveness of land owners to the carbon price has a measured impact on carbon sequestration. For example, under the fastest land use change scenario, carbon sequestration reaches 29.93 Mt CO2e by 2050 compared to 23.41 Mt CO2e in the no land use change scenario (a 28% increase). Even under the slowest land use change scenario, carbon sequestration is 25.89 Mt CO2e by 2050 (an 11% increase compared with no land use change). This is because, if foresters decide not to switch to permanent forests in 1 year, carbon prices and ultimately incentives to convert to permanent forests will be higher in future years.

Part of this research was completed while co-author Dominic White was visiting the MIT Joint Program.

Environmental Impacts of the Inflation Reduction Act - Documentation of the USREP-ReEDS Model Analysis

Authors' Impact/Purpose: This report documents methods used to analyze the economic and environmental impacts of the Inflation Reduction Act (IRA), enacted in August 2022 by the United States Congress. The analysis relies on the U.S. Regional Energy Policy (USREP) economy-wide model developed by researchers at the Massachusetts Institute of Technology (MIT), linked with the Regional Energy Deployment System (ReEDS) electricity sector model developed by researchers at the National Renewable Energy Laboratory (NREL).

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