首页> 外文OA文献 >Hotspots of uncertainty in land-use and land-cover change projections: a global-scale model comparison
【2h】

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

机译:土地利用和土地覆盖变化预测中不确定性的热点:全球规模的模型比较

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务,本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文,但由于OA文献来源多样且变更频繁,仍可能出现获取不到、文献不完整或与标题不符等情况,如果获取不到我们将提供退款服务。请知悉。

摘要

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 from11 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. Fromthis 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.
机译:在环境评估中经常使用基于模型的未来土地利用和土地覆被(LULC)变化的全球预测来研究LULC变化对环境服务的影响并为政策提供决策支持,这些预测的特点是不确定性很高在数量和预计变化的分配方面,可能会严重影响环境评估的结果。在这项研究中,我们基于11个全球规模的LULC变化模型的43个模拟,确定了不确定性的热点,这些模拟代表了对未来生物物理和社会经济状况的广泛假设。基于回归分析和方差分析,我们将不确定性的要素归因于输入数据,模型结构,场景故事情节和残差项。从这套多样化的模型和方案中,我们发现不确定性会有所不同,具体取决于所考虑的区域和LULC类型。不确定性的热点主要出现在全球重要生物群落的边缘(例如,北方和热带森林)。我们的结果表明,森林和牧场地区不确定性的重要来源来自模型中使用的不同输入数据。相比之下,农田在开始条件之间更为一致,而由于不同的情景假设和不同的建模方法,预计的变化会随着时间逐渐增加。网格单元级别的比较表明,分歧主要与LULC类型定义和各个模型分配方案有关。我们得出结论,在建模过程中提高观测数据的质量和一致性,以及改善LULC变化模型的分配机制仍然是重要的挑战。当前LULC在环境评估中的代表性可能会错过因LULC变化建模方法的多样性而引起的不确定性,许多研究忽略了LULC预测对LULC变化对气候,水资源或生物多样性影响的评估中的不确定性。

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号