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This paper applies methods of multiple resolution map comparison to quantify characteristics for 13 applications of 9 different popular peer-reviewed land change models. Each modeling application simulates change of land categories in raster maps from an initial time to a subsequent time. For each modeling application, the statistical methods compare: (1) a reference map of the initial time, (2) ence map of the subsequent time, and (3) a prediction map of the subsequent time. The three possible two-map comparisons for each application characterize: (1) the dynamics of the landscape, (2) the behavior of the model, and (3) the accuracy of the prediction. The three-map comparison for each application specifies the amount of the prediction's accuracy that is attributable to land persistence versus land change. Results show that the amount of error is larger than the amount of correctly predicted change for 12 of the 13 applications at the resolution of the raw data. The applications are summarized and compared using two statistics: the null resolution and the figure of merit. According to the figure of merit, the more accurate applications are the 123Comparing the input, output, and validation maps for several models of land change 13 ones where the amount of observed net change in the reference maps is larger. This paper facilitates communication among land change modelers, because it illustrates the range of results for a variety of models using scientifically rigorous, generally applicable, and intellectually accessible statistical techniques. JEL Classification

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Long-term global scenarios have underpinned research and assessment of global environmental change for four decades. Over the past ten years, the climate change research community has developed a scenario framework combining alternative futures of climate and society to facilitate integrated research and consistent assessment to inform policy. Here we assess how well this framework is working and what challenges it faces. We synthesize insights from scenario-based literature, community discussions and recent experience in assessments, concluding that the framework has been widely adopted across research communities and is largely meeting immediate needs. However, some mixed successes and a changing policy and research landscape present key challenges, and we recommend several new directions for the development and use of this framework.

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