Hotspots of uncertainty in land‐use and land‐cover change projections: a global‐scale model comparison

Abstract

Model‐based global projections of future land‐use and land‐cover (LULC) change are frequently used in environmental assessments to study the impact of LULC change on environmental services and to provide decision support for policy. These projections are characterized by a high uncertainty in terms of quantity and allocation of projected changes, which can severely impact the results of environmental assessments. In this study, we identify hotspots of uncertainty, based on 43 simulations from 11 global‐scale LULC change models representing a wide range of assumptions of future biophysical and socioeconomic conditions. We attribute components of uncertainty to input data, model structure, scenario storyline and a residual term, based on a regression analysis and analysis of variance. From this diverse set of models and scenarios, we find that the uncertainty varies, depending on the region and the LULC type under consideration. Hotspots of uncertainty appear mainly at the edges of globally important biomes (e.g., boreal and tropical forests). Our results indicate that an important source of uncertainty in forest and pasture areas originates from different input data applied in the models. Cropland, in contrast, is more consistent among the starting conditions, while variation in the projections gradually increases over time due to diverse scenario assumptions and different modeling approaches. Comparisons at the grid cell level indicate that disagreement is mainly related to LULC type definitions and the individual model allocation schemes. We conclude that improving the quality and consistency of observational data utilized in the modeling process and improving the allocation mechanisms of LULC change models remain important challenges. Current LULC representation in environmental assessments might miss the uncertainty arising from the diversity of LULC change modeling approaches, and many studies ignore the uncertainty in LULC projections in assessments of LULC change impacts on climate, water resources or biodiversity.

Anthropogenic land use estimates for the Holocene – HYDE 3.2

“…Analysis by Prestele et al (2016) show that it is quite crucial for future outcomes to determine at what spatial starting point global change models and integrated assessment models start their simulations. Therefore, we have also experimented with a different choice for the cropland and grazing land reference maps to see whether it would have an influence, and if so, to what extent the spatial patterns of land use would change.…”

Assessing uncertainties in global cropland futures using a conditional probabilistic modelling framework

“…A range of different models and scenarios have been used to project future cropland areas to 2100 with estimates in the range of 930 to 2670 Mha Prestele et al, 2016). This compares with today's cropland areas of around 1530 Mha.…”

“…We apply these scenarios to a global-scale, socio-economic model of agricultural land use change (the Parsimonious Land Use Model, PLUM; Engström et al, 2016) in combination with crop yield time series derived from the dynamic global vegetation model, LPJ-GUESS (Lund-Potsdam-Jena General Ecosystem Simulator; Lindeskog et al, 2013;Smith et al, 2001). PLUM has been benchmarked against different models and scenario studies in a land use model intercomparison exercise Prestele et al, 2016) that has demonstrated its consistency in comparison with other global cropland simulations. Because of its rapid runtimes, PLUM can explore uncertainties across its input parameter space and hence is appropriate for use in probabilistic simulations requiring multiple model iterations.…”

Accounting for monogastric livestock as a driver in global land use and cover change assessments

“…It is obvious that our way of representing monogastric livestock systems and including the demand for products from such systems is rather simplified and not capturing the diversity in these land systems. At the same time, the results indicate the potential impact of not accounting for these systems in scenario assessments, adding to the already high uncertainty in such global-scale assessments (Prestele et al, 2016). The choice of characteristics used to characterize land should be consistent with the questions asked in a LUCC assessment, and therefore may vary depending on the study.…”

“…Some grasslands, for example, are used as pastures, while others remain unused. Confusion between land cover and land use has led to large inconsistencies between global land use datasets and model results (Feddema et al, 2005;Prestele et al, 2016). Some recently developed characterizations of the Earth's surface also include land management intensity, in addition to land cover and land use (Nachtergaele & Petri, 2008;Van Asselen & Verburg, 2012).…”