Complex environmental model outputs used to inform decisions often have systematic errors and are of inappropriate resolution, requiring downscaling and bias correction for local applications. Here we provide a new interpretation of dasymetric modelling (DM) as a spatial bias correction framework useful in environmental modelling. DM is based on areal interpolation where estimates of some variable at target zones are obtained from overlapping source zones using ancillary information. We explore DM by downscaling runoff output from a distributed hydrological model using two meta-models and describe the properties of the methodology in detail. Consistent with properties of linear scaling bias correction, results show that the methodology 1) reduces errors compared to the source data and meta-models, 2) improve the spatial structure of the estimates, and 3) improve the performance of the downscaled estimates, particularly where meta-models perform poorly. The framework is simple and useful in ensuring spatial coherence of downscaled products.

用于为决策提供信息的复杂环境模型输出通常存在系统错误并且分辨率不合适，需要针对本地应用进行缩减和偏差校正。在这里，我们提供了对 dasymetric 建模 (DM) 作为环境建模中有用的空间偏差校正框架的新解释。DM 基于区域插值，其中目标区域的某些变量的估计值是使用辅助信息从重叠的源区域获得的。我们通过使用两个元模型缩小分布式水文模型的径流输出来探索 DM，并详细描述该方法的特性。与线性缩放偏差校正的特性一致，结果表明该方法 1) 与源数据和元模型相比减少了错误，2) 改善了估计的空间结构，3）提高缩小估计的性能，特别是在元模型表现不佳的情况下。该框架在确保缩小产品的空间一致性方面简单且有用。