Robert Link

Efficient multi-objective calibration of a computationally intensive hydrologic model with parallel computing software in Python

With enhanced data availability, distributed watershed models for large areas with high spatial and temporal resolution are increasingly used to understand water budgets and examine effects of human activities and climate change/variability on water resources. Developing parallel computing software to improve calibration efficiency has received growing attention of the watershed modeling community as it is very time demanding to run iteratively complex models for calibration. In this research, we introduce a Python-based parallel computing package, PP-SWAT, for efficient calibration of the Soil and Water Assessment Tool (SWAT) model. This software employs Python, MPI for Python (mpi4py) and OpenMPI to parallelize A Multi-method Genetically Adaptive Multi-objective Optimization Algorithm (AMALGAM), allowing for simultaneously addressing multiple objectives in calibrating SWAT. Test results on a Linux computer cluster showed that PP-SWAT can achieve a speedup of 45-109 depending on model complexity. Increasing the processor count beyond a certain threshold does not necessarily improve efficiency, because intensified resource competition may result in an I/O bottleneck. The efficiency achieved by PP-SWAT also makes it practical to implement multiple parameter adjustment schemes operating at different scales in affordable time, which helps provide SWAT users with a wider range of options of parameter sets to choose from for model(s) selection. PP-SWAT was not designed to address errors associated with other sources (e.g. model structure) and cautious supervision of its power should be exercised in order to attain physically meaningful calibration results. Integrating Python, mpi4py, OpenMPI to efficiently perform parallel calibration of SWAT using AMALGAM.Enhancing calibration by applying multipliers and addends at higher resolutions than is typically done for SWAT.Discussion of implications of model structure setup for SWAT calibration.

Soil Carbon Change and Net Energy Associated with Biofuel Production on Marginal Lands: A Regional Modeling Perspective

The use of marginal lands for biofuel production has been proposed as a promising solution for meeting biofuel demands while avoiding food-feed-fuel conflicts. However, uncertainty surrounds whether marginal lands can be reliably located, as well as their inherent biofuel potential and the possible environmental impacts. We developed a quantitative approach that integrates high-resolution land cover and land productivity to classify productive croplands and nonarable marginal lands in a nine-county region in southern Michigan. The classified lands were then examined with the spatially explicit modeling framework using the Environmental Policy Integrated Climate (EPIC) model to estimate net energy (NE) and soil organic carbon (SOC) changes associated with the cultivation of different annual and perennial production systems. Simulation results suggest that biofuel production systems underperform on marginal lands when compared to productive croplands. However, we found perennial grasses could perform better than annual crops. Hence, when growing perennial bioenergy crops on marginal lands instead of productive croplands, less additional land (about 0.09 ha per each hectare planted) would be needed to achieve the same NE than if growing annual bioenergy crops (additional 0.17 ha per hectare planted). Miscanthus ( × ) and switchgrass ( L.) can produce 112.43 and 74.61 GJ ha yr NE, respectively, and have the potential to sequester, on average, 0.59 and 0.23 Mg C ha yr SOC, respectively. Notably, simulation results indicate substantial variability of the NE and SOC storage potential across the study region. Thus, although perennial energy crops are promising options for biofuel production on marginal lands, given the large spatial variability, regional- and site-specific management strategies are required for sustainable biofuel production.

Role of the Freight Sector in Future Climate Change Mitigation Scenarios

The freight sectors role is examined using the Global Change Assessment Model (GCAM) for a range of climate change mitigation scenarios and future freight demand assumptions. Energy usage and CO2 emissions from freight have historically grown with a correlation to GDP, and there is limited evidence of near-term global decoupling of freight demand from GDP. Over the 21st century, greenhouse gas (GHG) emissions from freight are projected to grow faster than passenger transportation or other major end-use sectors, with the magnitude of growth dependent on the assumed extent of long-term decoupling. In climate change mitigation scenarios that apply a price to GHG emissions, mitigation of freight emissions (including the effects of demand elasticity, mode and technology shifting, and fuel substitution) is more limited than for other demand sectors. In such scenarios, shifting to less-emitting transportation modes and technologies is projected to play a relatively small role in reducing freight emissions in GCAM. By contrast, changes in the supply chain of liquid fuels that reduce the fuel carbon intensity, especially deriving from large-scale use of biofuels coupled to carbon capture and storage technologies, are responsible for the majority of freight emissions mitigation, followed by price-induced reduction in freight demand services.

Xanthos – A Global Hydrologic Model

Xanthos is an open-source hydrologic model, written in Python, designed to quantify and analyse global water availability. Xanthos simulates historical and future global water availability on a monthly time step at a spatial resolution of 0.5 geographic degrees. Xanthos was designed to be extensible and used by scientists that study global water supply and work with the Global Change Assessment Model (GCAM). Xanthos uses a user-defined configuration file to specify model inputs, outputs and parameters. Xanthos has been tested using actual global data sets and the model is able to provide historical observations and future estimates of renewable freshwater resources in the form of total runoff. Funding statement: PNNL is operated for DOE by Battelle Memorial Institute under contract DE-AC05-76RL01830.

Web-Based Visualization of the Global Change Assessment Model

The Global Change Assessment Model (GCAM) is an integrated assessment tool for exploring consequences and responses to global change. However, the current iteration of GCAM relies on NetCDF file outputs which need to be output from the model and then exported for visualization and analysis purposes. Such a requirement limits the uptake of this modeling platform for analysts that may wish to explore future scenarios. In this work, we present a web-based geovisual analytics interface for GCAM. Challenges of this work include enabling both domain expert and model experts to be able to functionally explore the model. We explore the use of various aggregation layers, coordinated views and how choices of icons can enhance the understanding of the underlying data. Our primary focus is on understanding the simulated impact of climate change on sectors of the global economy. To demonstrate our work, we present a case study which explores the potential impact that the China south-north water transportation project in the Yangtze River basin will have on projected water demands.

Energy-Water-Land Nexus in Latin America and the Caribbean: A Perspective from the Paris Agreement Climate Mitigation Pledges

The concept of the energy, water, and land (EWL) nexus encompasses a growing concern on the availability of vital resources derived from these intertwined systems, and how to manage the nexus resources to respond to the challenges posed by future human demands, aggravated by the perspective of climate change. This paper explores how the Paris Pledges might influence the EWL nexus in Latin America. In particular, it explores the near-term and long-term implications of the Paris pledges on the EWL nexus in four of the major countries/economies in Latin America: Argentina, Brazil, Colombia and Mexico. For this purpose, we employ the Global Change Assessment Model (GCAM), a state-of-the-art integrated assessment model of human and natural processes that captures the national-level EWL synergies and tradeoffs and allows the understanding of the key drivers of the EWL sectoral interactions and the role of climate policies such as the Nationally Determined Contributions (NDCs) that are included in the Paris pledges. Our findings indicate that under the emissions mitigation scenarios explicitly modeled to represent the Paris pledges framework, potential conflicts regarding the use of nexus resources in the four focus Latin American countries may be exacerbated by the induced changes in the energy and land sectors that would impinge upon the water sector of those nations. Despite the differential implications of the Paris pledges on each country, increased water demands for crop and biomass irrigation and for electricity generation were identified as the potential sources of the nexus conflicts that may emerge under this climate policy scenario. Hence, this study underscores the need to refine national climate policies within a EWL nexus resource planning framework such that a balance between the rational use of the nexus resources and stringent climate policies can be found.

GCAM v5.1: Representing the linkages between energy, water, land, climate, and economic systems

This paper describes GCAM v5.1, an open source model that represents the linkages between energy, water, land, climate, and economic systems. GCAM is a market equilibrium model, is global in scope, and operates from 1990 to 2100 in 5-year time steps. It can be used to examine, for example, how changes in population, income, or technology cost might alter crop production, energy demand, or water withdrawals, or how changes in one region’s demand for energy affect energy, water, and land in other regions. This paper describes the model, including its assumptions, inputs, and outputs. We then use 11 scenarios, varying the socioeconomic and climate policy assumptions, to illustrate the results from the model. The resulting scenarios demonstrate a wide range of potential future energy, water, and land uses. We compare the results from GCAM v5.1 to historical data and to future scenario simulations from earlier versions of GCAM and from other models. Finally, we provide information on how to obtain the model.

A Global Food Demand Model For The Assessment Of Complex Human-Earth Systems

Demand for agricultural products is an important problem in global change economics. Food consumption will shape and be shaped by global change through interactions with bioenergy and afforestation, two critical issues in meeting international goals. We develop a model of food demand for staple and nonstaple commodities that evolves with changing incomes and prices. The model addresses a long-standing issue in estimating food demands, the evolution of demand relationships across large changes in income and prices. We discuss the model, some of its properties and limitations. We estimate parameter values using pooled cross-sectional-time-series observations and Bayesian Monte Carlo method and cross-validate the model by estimating parameters using a subset of the observations and test its ability to project into the unused observations. Finally, we apply bias correction techniques borrowed from the Earth system modeling community and report results. We find that the demand for food rises rapidly as income initially increases from zero. Demand for staples peaks at under

The SSP4: A world of deepening inequality

1000 per person per capita. Nonstaple food demands increase steadily with income. While staples are an inferior good at per capita incomes greater than

A simple object-oriented and open-source model for scientific and policy analyses of the global climate system – Hector v1.0

1000, we see no evidence that there is a range of per capita income for which staples are Giffen goods.