Multi-factor,multi-state,multi-model scenarios: Exploring food and climate futures for Southeast Asia

Decision-makers aiming to improve food security, livelihoods and resilience are faced with an uncertain future. To develop robust policies they need tools to explore the potential effects of uncertain climatic, socioeconomic, and environmental changes. Methods have been developed to use scenarios to present alternative futures to inform policy. Nevertheless, many of these can limit the possibility space with which decision-makers engage. This paper will present a participatory scenario process that maintains a large possibility space through the use of multiple factors and factor-states and a multi-model ensemble to create and quantify four regional scenarios for Southeast Asia. To do this we will explain 1) the process of multi-factor, multi-state building was done in a stakeholder workshop in Vietnam, 2) the scenario quantification and model results from GLOBIOM and IMPACT, two economic models, and 3) how the scenarios have already been applied to diverse policy processes in Cambodia, Laos, and Vietnam.     

Impact of climate model uncertainties on socioeconomics: A case study with a medium mitigation scenario

Carbon dioxide (CO₂) emissions are strongly associated with economy. The amount of CO₂ that human society can emit in order to achieve a climate target depends on physical and biogeochemical properties in the climate system; these vary among climate models or earth system models (ESMs). Thus, uncertainties in such models, the spread remained when we both consider the range of existing models and observational data for key variables, can affect analysis of future global economy. In this study, using a computable general equilibrium model, we analyze the impacts on socioeconomics under a medium climate mitigation scenario by following three emission pathways considering uncertainties in existing ESMs (the lower and upper bounds as well as the mean). The results indicate that the impacts are larger in the lower bound case, despite the fact that economic and energy demands will increase continuously. In a comparison between the upper and lower bound cases, the carbon price of the latter case is approximately three times higher than that of the former case in 2100. Consequently, primary/final energy demand in the lower bound case becomes 1.0%/14% lower, and more renewables and carbon capture and storage are required to be used. Furthermore, the gross domestic product in the lower bound case is 4.1% smaller. Thus, within the scenario, the socioeconomic impacts caused by ESM uncertainties are not insignificant, but are smaller than the differences in annual and cumulative emissions.     

A daily weather generator for use in climate change studies

This paper describes the development of a weather generator for use in climate impact assessments of agricultural and water system management. The generator produces internally consistent series of meteorological variables including: rainfall, temperature, humidity, wind, sunshine, as well as derivation of potential evapotranspiration. The system produces series at a daily time resolution, using two stochastic models in series: first, for rainfall which produces an output series which is then used for a second model generating the other variables dependent on rainfall. The series are intended for single sites defined nationally across the UK at a 5 km resolution, but can be generated to be representative across small catchments (<1000 km²). Scenarios can be generated for the control period (1961–1990) based on observed data, as well as for the UK Climate Impacts Programme (UKCIP02) scenarios for three time slices (2020s, 2050s and 2080s). Future scenarios are generated by fitting the models to observations which have been perturbed by application of change factors derived from the UKCIP02 mean projected changes in that variable. These change factors are readily updated, as new scenarios become available, and with suitable calibration data the approach could be extended to any geographical region.     

Identifying parametric controls and dependencies in integrated assessment models using global sensitivity analysis

Integrated assessment models for climate change (IAMs) couple representations of economic and natural systems to identify and evaluate strategies for managing the effects of global climate change. In this study we subject three policy scenarios from the globally-aggregated Dynamic Integrated model of Climate and the Economy IAM to a comprehensive global sensitivity analysis using Sobol' variance decomposition. We focus on cost metrics representing diversions of economic resources from global world production. Our study illustrates how the sensitivity ranking of model parameters differs for alternative cost metrics, over time, and for different emission control strategies. This study contributes a comprehensive illustration of the negative consequences associated with using a priori expert elicitations to reduce the set of parameters analyzed in IAM uncertainty analysis. The results also provide a strong argument for conducting comprehensive model diagnostics for IAMs that explicitly account for the parameter interactions between the coupled natural and economic system components.     

Integrated modelling of urban spatial development under uncertain climate futures: A case study in Hungary

To provide fundamental decision support information for climate risk assessment in Hungary, an urban spatial development model of land cover change and population age structure dynamics was developed and applied to local integrated scenarios of climate change and stakeholder-derived socio-economic change. The four integrated scenarios for Hungary produced contrasting projections for urban patterns to 2100, but peri-urbanisation around Budapest was estimated to occur under all scenarios, together with a decline in working age population in the centres of the capital and major towns. This suggests that future urban planning needs to take into consideration the potential for underutilised urban infrastructure in the centre of the capital and pressures for social service provisioning in its outskirt. The integrated scenarios and model developed can be used in future studies to test the effectiveness of inter-sectoral policy responses in adapting urban planning to multiple climate and socio-economic challenges.     

Evaluating four downscaling methods for assessment of climate change impact on ecological indicators

Assessments of climate change impacts on freshwater ecosystems are generally based on global climate models (GCMs) and ecologically relevant “time-averaged” hydrological indicators derived from long-term records. Although uncertainties from GCMs have been recognized, the influence of downscaling methods remains unclear. This paper evaluates the influence of applying different downscaling methods of increasing complexity (annual scaling, monthly scaling, quantile scaling, and weather generator method) on the assessment of ecological outcomes. In addition to time-averaged indicators, “sequence-dependent” metrics which involve ecological dynamics by considering the impacts of flow sequencing are also adopted. In a case study in Australia, the condition of river red gum forest was assessed. Results show that the choice of downscaling methods can be of similar importance as that of GCMs in ecological impact studies. Where sequence-dependent metrics are adopted, more sophisticated downscaling techniques should be used to better represent changes in the frequency and sequence of flow events.     

Evaluating methods to simulate crop rotations for climate impact assessments – A case study on the Canterbury plains of New Zealand

Most climate impact assessments for food production simulate single crops with re-initialised soil conditions. However, crop rotations with multiple crops are used in many agricultural regions worldwide. This case-study compares methods to aggregate outputs from simulations of multi-crop systems for climate impact assessments. The APSIM model was used to simulate four crops as monocultures (re-initialised or continuous) or as (single or multiple) instances of continuous rotations. We considered two contrasting climates and two soil types, with four production intensification scenarios (high/low water and nitrogen input). Results suggest that differences among the methods depend on the impact variable of interest and the degree of intensification. Detailed simulations (i.e. multiple runs of continuous rotations) were especially valuable for soil-related variables and limiting growth conditions. These results can indicate sources of uncertainty for large scale impact and adaptation assessments where simplifications of crop rotations are often necessary.     

An interdisciplinary modelling framework for selecting adaptation measures at the river basin scale in a global change scenario

Shaping global change adaptation strategy in water resource systems requires an interdisciplinary approach to deal with the multiple dimensions of the problem. The modelling framework presented integrates climate, economic, agronomic and hydrological scenarios to design a programme of adaptation measures at the river basin scale. Future demand scenarios, combined with a down-scaled climate scenario, provide the basis to estimate the demand and water resources in 2030. A least-cost river basin optimisation model is then applied to select adaptation measures ensuring that environmental and supply management goals are achieved. In the Orb river basin (France), the least-cost portfolio selected suggests mixing demand and supply side measures to adapt to global change. Trade-offs among the cost of the programme of measures, the deficit in agricultural water supply and the level of environmental flows are investigated. The challenges to implement such interdisciplinary approaches in the definition of adaptation strategies are finally discussed.     

An integrated modelling approach for the assessment of land use change effects on wastewater infrastructures

The simulation of sewage systems and wastewater treatment plants is strategic for assessing the effect of new dwellings on the existing water facilities. This paper introduces an integrated framework made by a land use change model, a sewage system simulator, and a wastewater treatment plant simulator. This is a complex system since each element is characterized by different dynamics. The land use change model simulates the annual expansion of an urban area according to planners’ guidelines; the sewage system simulator investigates the response of the drainage system to the expansion. The wastewater treatment plant is simulated in order to assess the impact of the new outflows on the existing plant. The three models are integrated into a Simulink model. Two components of the developed framework are based on models well established in literature. The proposed framework is tested on a simple case study of a small town located in south west of Scotland.     

The persistence of shocks in GDP and the estimation of the potential economic costs of climate change

Integrated assessment models (IAMs) typically ignore the impact climate change could have on economic growth. The damage functions of these models assume that climate change impacts have no persistence at all, affecting only the period when they occur. Persistence of shocks is a stylized fact of macroeconomic time series and it provides a mechanism that could justify larger losses from climate change than previously estimated. Given that the degree of persistence of climate impacts is unknown, we analyze the persistence of generic shocks in observed GDP series for different world regions and compare it to that of the leading IAMs. Under the working hypothesis of interpreting the direct impact of climate change as such shocks, the implications for growth are investigated for two RCP scenarios. The way of introducing climate shocks to GDP in most IAMs can be interpreted as assuming an autonomous, costless, large and effective reactive adaptation capacity.     

A web-based tool for the development of Intensity Duration Frequency curves under changing climate

Intensity Duration Frequency (IDF) curves are among the most common tools used in water resources management. They are derived from historical rainfall records under the assumption of stationarity. Change of climatic conditions makes the use of historical data for development of IDFs for the future unjustifiable. The IDF_CC, a web based tool, is designed, developed and implemented to allow local water professionals to quickly develop estimates related to the impact of climate change on IDF curves for almost any local rain monitoring station in Canada. The primary objective of the presented work was to standardize the IDF update process and make the results of current research on climate change impacts on IDF curves accessible to everyone. The tool is developed in the form of a decision support system (DSS) and represents an important step in increasing the capacity of Canadian water professionals to respond to the impacts of climate change.     

Coupled component modelling for inter- and transdisciplinary climate change impact research: Dimensions of integration and examples of interface design

In environmental research the importance of interfaces between the traditional knowledge fields in natural and social sciences is increasingly recognized. In coupled component modelling, the process of developing interface designs can support the communicative, social and cognitive integration between representatives of different knowledge fields. The task of integration is thereby not merely an additive procedure but has to be considered as important part of the research process. In our application, the development of a coupled component model facilitated an integrative assessment of the impact of climate change on snow conditions and skiing tourism in a typical Austrian ski resort. We elaborate the integration on two abstraction levels, a theoretical one and an applied one related to the case study. Other than model output, results presented here relate to the inter- and transdisciplinary development of the coupled component model and its interface design. We show how scientists from various disciplines and representatives from diverse societal fields jointly design interface tools. We identify joint model development – taking into consideration the different dimensions of integration – and recursive modelling as keys for successful inter- and transdisciplinary integration. Such integrative interface science can provide new insights which go beyond the sum of what can be learned from its disciplinary components.     

A multi-objective optimization approach for integrated production planning under interval uncertainties in the steel industry

This paper investigates one of the key decision-making problems referring to the integrated production planning (IPP) for the steelmaking continuous casting-hot rolling (SCC-HR) process in the steel industry. The complexities of the practical IPP problem are mainly reflected in three aspects: large-scale decision variables; multiple objectives and interval-valued uncertain parameters. To deal with the difficulty of large-scale decision variables, we introduce a new concept named “order-set” for modeling. In addition, considering the multiple objectives and uncertainties of the given IPP problem, we construct a multi-objective optimization model with interval-valued objective functions to optimize the throughput of each process, the hot charge ratio of slabs, the utilization rate of tundishes and the additional cost of technical operations. Furthermore, we propose a novel approach based on a modified interval multi-objective optimization evolutionary algorithm (MI-MOEA) to solve the problem. The proposed model and algorithm were tested with daily production data from an iron and steel company in China. Computational experiments demonstrate that the proposed method generates quite effective and practical solutions within a short time. Based on the IPP model and MI-MOEA, an IPP system has been developed and implemented in the company.     

Remote sensing of biological soil crust under simulated climate change manipulations in the Mojave Desert

Earth's arid and semiarid ecosystems are subject to novel combinations of disruptive factors and unprecedented rates of change. Biotic soil crust is believed to be sensitive to impacts caused by land use and climate changes. This study examined the potential for spectral detection of different biological soil crusts (BSC: cyanobacteria, moss and lichen) and bare soil components at a long-term manipulative experiment at the Mojave Global Change Facility (MGCF) in southwestern Nevada. We evaluated the potential for spectral detection of experimental treatments using laboratory and field measured reflectance spectra in the second and third year of the experiment, and airborne hyperspectral data obtained in the third year of the manipulations for soil disturbance, increased summer rainfall, and dry nitrogen deposition. Laboratory spectra of individual components of biological soil crust and bare soil measured under controlled laboratory conditions were spectrally different over much of the spectrum and exhibited features at 0.42, 0.50, and 0.68 μm, which could differentiate these materials. Field measured spectra were more similar in overall shape in each of the MGCF treatments and individual BSC could not be distinguished. The field spectra most closely resemble cyanobacteria from laboratory measurements, which are known to cover up to 60% of the inter-shrub spaces. There were significant treatment differences between control, soil disturbance, and irrigation treatments in field spectral measurements and a spectral feature in the 2.00-2.08 μm region could distinguish these treatments. The treatments were also apparent in high spatial resolution (~ 4 m ground IFOV) airborne hyperspectral imagery using a minimum noise fraction (MNF) analysis, although treatments were not distinct in terms of laboratory or field-based specific features. Disturbance treatments were easily apparent in color-infrared imagery although other treatments were not distinguished. Three-band composites from a MNF analysis and classification images of the six most significant MNF bands for treatment differences, revealed disturbed and irrigation treatments and combinations of these with nitrogen treatments can be observed but control treatments were not separated from the untreated background. Nitrogen treatments were generally not significantly different from controls unless combined with irrigation or disturbance treatments. These data suggest that hyperspectral imagery could be used to monitor local and perhaps regional changes in biological soil crust in the southwestern deserts of the United States, even if crust components are not individually detected.     

Applications of Bayesian belief networks in water resource management: A systematic review

Bayesian belief networks (BBNs) are probabilistic graphical models that can capture and integrate both quantitative and qualitative data, thus accommodating data-limited conditions. This paper systematically reviews applications of BBNs with respect to spatial factors, water domains, and the consideration of climate change impacts. The methods used for constructing and validating BBN models, and their applications in different forms of decision-making support are examined. Most reviewed publications originate from developed countries (70%), in temperate climate zones (42%), and focus mainly on water quality (42%). In 60% of the reviewed applications model validation was based on the expert or stakeholder evaluation and sensitivity analysis, and whilst in 27% model performance was not discussed. Most reviewed articles applied BBNs in strategic decision-making contexts (52%). Integrated modelling tools for addressing challenges of dynamically complex systems were also reviewed by analysing the strengths and weaknesses of BBNs, and integration of BBNs with other modelling tools.     

A model-based integrated assessment of land degradation by water erosion in a valuable Spanish rangeland

This paper presents an integrated assessment model aimed at evaluating land degradation by water erosion in dehesa rangelands in the Iberian Peninsula. The model is built following the system dynamics approach. The degradation risk is likened to the probability of losing a certain amount of soil within a number of years, as estimated over a great number of stochastic simulations. Complementary indicators are the average times needed to lose different amounts of soil over the simulations. A group of exogenous factors are ranked in order of importance. These factors are mainly climatic and economic and potentially affect soil erosion. Calibration is carried out for a typical dehesa defined over 22 working units selected from 10 representative farms distributed throughout the Spanish region of Extremadura. The degradation risk turns out to be moderate. The importance of climatic factors on soil erosion considerably exceeds that of those linked to human activities.     

A decision support system for updating and incorporating climate change impacts into rainfall intensity-duration-frequency curves: Review of the stakeholder involvement process

Stakeholder involvement can serve to increase the quality of decision support systems (DSSs) and increase the perceived legitimacy of DSS outputs. Involving those who are ultimately affected by the outputs of DSSs in system design and development also reflects democratic principles. Importantly, stakeholder involvement can help ensure that the outputs of DSSs are used in decision-making processes. However, DSSs often fail due to poor engagement of stakeholder and end-user communities in the development and design of systems. The stakeholder engagement process applied in the development of the Computerized Tool for the Development of Intensity Duration Frequency Curves under Climate Change described here followed many of the tenants of best practices identified in the literature. While the engagement strategy was generally considered successful, over- and under-representation of some stakeholder groups and long term funding issues were weaknesses in the engagement process.     

A comprehensive system for semantic spatiotemporal assessment of risk in urban areas

Risk assessment of urban areas aims at limiting the impact of harmful events by increasing awareness of their possible consequences. Qualitative risk assessment allows to figure out possible risk situations and to prioritize them, whereas quantitative risk assessment is devoted to measuring risks from data, in order to improve preparedness in case of crisis situations. We propose an automatic approach to comprehensive risk assessment. This leverages on a semantic and spatiotemporal representation of knowledge of the urban area and relies on a software system including: a knowledge base; two components for quantitative and qualitative risk assessments, respectively; and a WebGIS interface. The knowledge base consists of the TERMINUS domain ontology, to represent urban knowledge, and of a geo‐referenced database, including geographical, environmental and urban data as well as temporal data related to the levels of operation of city services. CIPcast DSS is the component devoted to quantitative risk assessment, and WS‐CREAM is the component supporting qualitative risk assessment based on computational creativity techniques. Two case studies concerning the city of Rome (Italy) show how this approach can be used in a real scenario for crisis preparedness. Finally, we discuss issues related to plausibility of risks and objectivity of their assessment.     

A framework for coupling explanation and prediction in hydroecological modelling

Causal explanation and empirical prediction are usually addressed separately when modelling ecological systems. This potentially leads to erroneous conflation of model explanatory and predictive power, to predictive models that lack ecological interpretability, or to limited feedback between predictive modelling and theory development. These are fundamental challenges to appropriate statistical and scientific use of ecological models. To help address such challenges, we propose a novel, integrated modelling framework which couples explanatory modelling for causal understanding and input variable selection with a machine learning approach for empirical prediction. Exemplar datasets from the field of freshwater ecology are used to develop and evaluate the framework, based on 267 stream and river monitoring stations across England, UK. These data describe spatial patterns in benthic macroinvertebrate community indices that are hypothesised to be driven by meso-scale physical and chemical habitat conditions. Whilst explanatory models developed using structural equation modelling performed strongly (r² for two macroinvertebrate indices = 0.64–0.70), predictive models based on extremely randomised trees demonstrated moderate performance (r² for the same indices = 0.50–0.61). However, through coupling explanatory and predictive components, our proposed framework yields ecologically-interpretable predictive models which also maintain the parsimony and accuracy of models based on solely predictive approaches. This significantly enhances the opportunity for feedback among causal theory, empirical data and prediction within environmental modelling.     

Advancements in the design and validation of an air pollution integrated assessment model for Spain

This paper describes the design and application of the Atmospheric Evaluation and Research Integrated model for Spain (AERIS). Currently, AERIS can provide concentration profiles of NO₂, O₃, SO₂, NH₃, PM, as a response to emission variations of relevant sectors in Spain. Results are calculated using transfer matrices based on an air quality modelling system (AQMS) composed by the WRF (meteorology), SMOKE (emissions) and CMAQ (atmospheric-chemical processes) models. The AERIS outputs were statistically tested against the conventional AQMS and observations, revealing a good agreement in both cases. At the moment, integrated assessment in AERIS focuses only on the link between emissions and concentrations. The quantification of deposition, impacts (health, ecosystems) and costs will be introduced in the future. In conclusion, the main asset of AERIS is its accuracy in predicting air quality outcomes for different scenarios through a simple yet robust modelling framework, avoiding complex programming and long computing times.