Chapter 4 State of the Art in Simulating Future Changes in Ecosystem Services Coordinating Lead Author: Peter Kareiva Lead Authors: John B. R. Agard, Jacqueline Alder, Elena Bennett, Colin Butler, Steve Carpenter, W. W. L. Cheung, Graeme S. Cumming, Ruth Defries, Bert de Vries, Robert E. Dickinson, Andrew Dobson, Jonathan A. Foley, Jacqueline Geoghegan, Beth Holland, Pavel Kabat, Juan Keymer, Axel Kleidon, David Lodge, Steven M. Manson, Jacquie McGlade, Hal Mooney, Ana M. Parma, Miguel A. Pascual, Henrique M. Pereira, Mark Rosegrant, Claudia Ringler, Osvaldo E. Sala, B. L. Turner II, Detlef van Vuuren, Diana H. Wall, Paul Wilkinson, Volkmar Wolters Review Editors: Robin Reid, Marten Scheffer, Antonio Alonso Main Messages . ............................................ 73 4.1 Introduction ........................................... 73 4.2 Forecasting Changes in Land Use and Land Cover .............. 74 4.2.1 Existing Approaches 4.2.2 Critical Evaluation of Approaches 4.2.3 Research Needs 4.3 Forecasting Impacts of Land Cover Change on Local and Regional Climates . ............................................ 77 4.3.1 Existing Approaches 4.3.2 Critical Evaluation of Approaches 4.3.3 Research Needs 4.4 Forecasting Change in Food Demand and Supply ............... 81 4.4.1 Existing Approaches 4.4.2 Critical Evaluation of Approaches 4.4.3 Research Needs 4.5 Forecasting Changes in Biodiversity and Extinction ............. 84 4.5.1 Existing Approaches 4.5.2 Critical Evaluation of Approaches 4.5.3 Research Needs 4.6 Forecasting Changes in Phosphorus Cycling and Impacts on Water Quality .............................................. 87 4.6.1 Existing Approaches 4.6.2 Critical Evaluation of Approaches 4.6.3 Research Needs 4.7 Forecasting Changes in the Nitrogen Cycle and Their Consequences . ........................................ 90 4.7.1 Existing Approaches 71 ................. 11411$ $CH4 10-27-05 08:41:10 PS PAGE 71 72 Ecosystems and Human Well-being: Scenarios 4.7.2 Critical Evaluation of Approaches 4.7.3 Research Needs 4.8 Forecasting Fish Populations and Harvest . ................. 94 4.8.1 Existing Approaches 4.8.2 Critical Evaluation of Approaches 4.8.3 Research Needs 4.9 Forecasting Impacts on Coastal Ecosystems . ................. 98 4.9.1 Existing Approaches 4.9.2 Critical Evaluation of Approaches 4.9.3 Research Needs 4.10 Forecasting Impacts on Human Health ....................... 101 4.10.1 Existing Approaches 4.10.2 Critical Evaluation of Approaches 4.10.3 Research Needs 4.11 Integrated Assessment Models ............................. 102 4.11.1 Existing Approaches 4.11.2 Critical Evaluation of Approaches 4.11.3 Conclusions 4.12 Key Gaps in Our Modeling Abilities ......................... 107 REFERENCES .............................................. 108 TABLES 4.1 Examples of Integrated Assessment Models ................. 11411$ $CH4 10-27-05 08:41:11 PS PAGE 72 State of the Art in Simulating Future Changes in Ecosystem Services 73 Main Messages next 10 years, which in turn could provide improved tools for future global ecosystem assessments. Hence we discuss Building scenarios and anticipating changes in ecosystem services are some models, such as for phosphorous cycling, for which modeling exercises. The reliability of models depends on their data inputs there is no global model but where progress is expected so and the models themselves. This chapter sketches out the state-of-the-art that future global assessments will have new tools. Climate modeling approaches for critical components of the Millennium Ecosystem As- modeling is not covered, since there have been numerous sessment scenarios, examines strengths and weaknesses and alternative ap- review papers describing the existing climate models. proaches, identifies critical uncertainties, and describes high-priority research In general we seek in this chapter not to advocate or that could resolve fundamental uncertainty. defend the MA’s choice of IMAGE, IMPACT, WaterGAP, In order to evaluate the MA scenarios, readers must understand the capa- and Ecosim/Ecopath. Rather, we provide readers with an bilities, uncertainties, and frontiers of the models used to project overview of the modeling field and the variety of ap- changes in ecosystem services. This chapter provides a rigorous scien- proaches being pursued, with pointers to where we expect tific discussion of just how confident we can be about different dimen- future research will lead. This venture is so new that there sions of the scenario model analyses and where we need to do a great is no commonly accepted suite of models or, as is the case deal more work. Uncertainty is acceptable as long as it is acknowledged up for climate models, some standard approach for testing and front. Although many of the models used to inform scenarios have weak- contrasting the performance of competing models. In some nesses, the alternative is to use no models whatsoever. The modeling ap- cases the actual models used by the MA are virtually all that proaches and the uncertainties vary according to topic. Hence we take up the is available. In other cases, such as in models for predicting modeling issues one topic at a time, forecasting land cover change, impacts of biodiversity change, there are a variety of options, all under land cover changes on local climates, changes in food demand and supply, current research development. changes in biodiversity and extinction rates, impacts of changes in phospho- The major types of models are: rous cycles, impacts of changes in nitrogen cycle and inputs, fisheries and • statistical models that rely on observed relationships and harvest, alterations of coastal ecosystems, and impacts on human health. The extrapolate into the future; final sections evaluate integrated assessment models and look at key gaps in • first-principle equations that solve for equilibrium or current modeling abilities. draw on fundamental laws of transport and mass balance; • The uncertainties and limitations of models are extensive, and in many large system (usually simulation) models that mathemati- cases proven methods do not exist for the forecasting tasks that we face. cally describe relationships among a web of state-variables Recurring limitations and constraints include an absence of models that work and attempt to include a somewhat complete representa- well across multiple scales, failure of models to couple interacting processes, tion of the drivers of change; and models based on nonrepresentative subsets of Earth’s ecosystem ser- • expert models and decision support systems that translate vices (such as specific and narrow taxonomic groups or geographic regions). qualitative insights or expertise into quantitative asser- These limitations do not mean we should not attempt to make a forecast—only tions; and that we should present results with appropriate levels of uncertainty. The act • a wide variety of ‘‘agent-based’’ or cellular automata of attempting to make forecasts where apt methods do not exist has already models in which the activities of individual actors are spurred enormous research and innovation, such that in five years our fore- simulated and then aggregated to understand whole- casts will become much more reliable. For this reason, we pay particular atten- system behavior. tion to advances in modeling or data that are likely to greatly enhance our This is not the only taxonomy of models; alternative ability to assess alternative ecosystem futures. It is important to recognize that distinctions include stochastic versus deterministic, simula- models are not statements of fact but instead are hypotheses to be evaluated tion versus analytical, spatial versus nonspatial, equilibrium in light of coming changes in ecosystem services. versus nonequilibrium, and so forth. But these categories are most germane to the strategic choices available when 4.1 Introduction attempting to perform a global scenario analysis. We have The models used to generate scenarios for the Millennium made an effort to remove as much technical language as Ecosystem Assessment are not the only models available. possible. We obviously have not succeeded as well as would This chapter examines state-of-the-art modeling ap- be ideal. However, in many cases what appears to be ‘‘jar- proaches for critical components of the MA scenarios. It gon’’ is necessary for precision and to help other technical considers the suite of models and modeling approaches that experts know exactly what modeling issue is under discus- might be drawn on for scenario analyses. The four core sion. models actually used for the global MA were IMAGE for This is not a chapter for light reading. This is a chapter land use change, IMPACT for food demand and agricul- to read with the idea of learning what is going on in the ture, WaterGAP for water use and availability, and EcoPath modeling world that might be important for future assess- and Ecosim for predicting fisheries impacts. (See Chapter 6 ments. The topics chosen are not encyclopedic. We struc- for more-detailed descriptions of the models.) In many cases ture discussion around core modeling arenas (such as these models were chosen because they are the only ones fisheries or land use or agricultural production). One of the with global coverage (WaterGAP, for instance). biggest areas of modeling research is coupling models of The state of the art for environmental modeling is different processes together and attempting to incorporate changing very rapidly. This chapter describes the key mod- feedbacks among processes. Figure 6.3 in Chapter 6 shows eling arenas in which we expect major advances over the how the MA linked together to model different processes. ................. 11411$ $CH4 10-27-05 08:41:11 PS PAGE 73 74 Ecosystems and Human Well-being: Scenarios In the future there may be many more options, and in fact 4.2.1 Existing Approaches one conclusion of this chapter is that the linking of different processes and scales is probably the biggest research need. Perhaps the simplest land change models use a non-iterative When considering each section, it will be obvious that set of equations to seek a single solution where the modeled none of the modeling approaches is ideal.