Maps of regulating urban ecosystem services (UES) aid identification of priority areas for green–blue infrastructure investment to improve urban resilience to environmental hazards. Current mapping approaches however may present coarse spatial resolutions, and often fail to consider how UES flows serve resident demand at the appropriate micro-scale. In addition, prohibitive costs involved in collecting primary data to validate UES model parameters to local conditions may enforce the use of proxy methods, thereby inferring ambiguity in parameterisation and uncertainty in mapping outputs. This study examines both issues through the implementation of a high-spatial resolution approach to map multiple urban regulating ecosystem service (temperature regulation, stormwater absorption, and carbon storage) deprivation in Manchester, UK. Poorly performing UES areas are defined as the lowest 10% combined ecosystem service indicator values (‘coldspots’) at 100m grid resolution. Coldspots are compared to population demand levels, disaggregated from weighted population estimates, indicating neighbourhoods deprived of UES. Ambiguity in proxy method implementation is examined using combinations of UES parameter settings (n = 16) within various demand measures (n = 3) to measure changes in relationships between UES, and variation in final map outputs across the study area. Uncertainty is therefore quantified as an interactive process, whereby input parameter ambiguity affects local uncertainty in map outputs, due to varying landcover composition. As explicit sensitivity analysis in current UES mapping studies is limited, the study demonstrates how ambiguity in method parameterisation may impact UES mapping exercises. Complex interactions governing spatial variance in map uncertainty may therefore be addressed through identification of consistent areas of interest (e.g. hotspots, coldspots) by contrasting outputs realised from different parameterisations. As such, the study demonstrates the mapping approach as a transferable city-wide visualisation tool, using accessible data and methods, to investigate regulating UES deprivation at practical scales required to retrofit existing urban infrastructure with green-blue infrastructure investment.

规范城市生态系统服务（UES）的地图有助于确定绿蓝基础设施投资的优先领域，以提高城市对环境危害的抵御能力。但是，当前的映射方法可能会呈现出粗糙的空间分辨率，并且常常无法考虑UES流如何在适当的微尺度上满足居民需求。另外，在收集主要数据以验证UES模型参数以适应当地条件时所涉及的高昂成本可能会强制使用代理方法，从而推断出参数化的歧义性和映射输出的不确定性。这项研究通过实施高空间分辨率方法来研究这两个问题，以绘制英国曼彻斯特的多种城市调节生态系统服务（温度调节，雨水吸收和碳储存）匮乏的情况。UES区域表现不佳被定义为100m网格分辨率下最低10％的综合生态系统服务指标值（“ coldspots”）。将寒冷地区与人口需求水平进行比较（从加权人口估计中得出），表明缺少UES的社区。使用UES参数设置的组合检查代理方法实施中的歧义（n = 16）在各种需求量度内（n = 3）以衡量UES之间关系的变化以及整个研究区域最终地图输出的变化。因此，不确定性被量化为一个交互过程，其中输入参数的不确定性会影响土地输出组成的变化，从而影响地图输出中的局部不确定性。由于当前UES映射研究中的显式敏感性分析有限，因此该研究表明方法参数化的歧义可能会如何影响UES映射练习。因此，通过对比从不同参数化实现的输出，可以通过确定一致的关注区域（例如热点，冷点）来解决控制地图不确定性中的空间变化的复杂相互作用。因此，本研究使用可访问的数据和方法，将地图绘制方法展示为一种可转让的城市范围可视化工具，