Data Release for The sensitivity of ecosystem service models to choices of input data and spatial resolution

Although ecosystem service (ES) modeling has progressed rapidly in the last 10-15 years, comparative studies on data and model selection effects have become more common only recently. Such studies have drawn mixed conclusions about whether different data and model choices yield divergent results. In this study we apply inter- and intra-model comparisons to address these questions at national and provincial scales in Rwanda. We compare results of (1) carbon, annual, and seasonal water yield using InVEST and WASSI models, and the above plus the InVEST sediment regulation model using (2) 30- and 300 m resolution data and (3) three different input land cover datasets. For the inter-model comparison, we found the two models to give diverging results, with most metrics being complementary rather than directly comparable. WASSI and simpler InVEST models (carbon storage and annual water yield) were relatively insensitive to the choice of spatial resolution, but more complex InVEST models (seasonal water yield and sediment regulation) yielded strong differences when applied at differing resolution. Over half of the models predicted national-scale ES similarly regardless of input land cover data. However, only the WASSI runoff model predicted similar national- and provincial-scale ES across all input datasets. Our results confirm and extend conclusions of past studies, showing that in certain cases (e.g., simpler models and national-scale analyses), results are robust to the choice of input data. For more complex models, those with different output metrics, and subnational to site-based analyses in heterogeneous environments, data and model choices strongly influence modeling results.