Data Release for The sensitivity of ecosystem service models to choices of input data and spatial resolution (ver. 1.1, June 2020)
The sensitivity of ecosystem service models to choices of input data and spatial resolution
Dates
Publication Date
2018-02-27
Time Period
2017
Last Revision
2020-06-05
Citation
Bagstad, K.J., Cohen, Erika, Ancona, Z.H., McNulty, S.G., and Sun, Ge, 2018, Data Release for The sensitivity of ecosystem service models to choices of input data and spatial resolution (ver. 1.1, June 2020): U.S. Geological Survey data release, https://doi.org/10.5066/F7CR5S92.
Summary
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 [...]
Summary
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.
Version 1.1: This metadata and accompanying data described has been updated to include the SERVIR 30 meter data for the study area. This includes the data for carbon storage, water yield, sediment retention, sediment export, quickflow and local recharge. They are all contained in the zip file InVEST SERVIR 30m Data.
Click on title to download individual files attached to this item.
Metadata_Sensitivity_Model_Choice_Version_1_1_June_2020.xml Original FGDC Metadata
View
33.42 KB
application/fgdc+xml
Appendix Input Data Tables.zip
6.53 KB
application/zip
InVEST CCI 2010 300m Data.zip
8.32 MB
application/zip
InVEST Globeland 300m Data.zip
7.47 MB
application/zip
InVEST SERVIR 2010 300m Data.zip
7.53 MB
application/zip
WASSI Data.zip
6.53 MB
application/zip
InVEST Globeland 2010 30m Data.zip
689.35 MB
application/zip
InVEST_SERVIR 2010 30m Data.zip
535.74 MB
application/zip
Version History 1.1.txt
1.16 KB
text/plain
Related External Resources
Type: Related Primary Publication
Bagstad, K.J., Cohen, Erika, Ancona, Z.H., McNulty, S.G., and Sun, Ge, 2018, The sensitivity of ecosystem service models to choices of input data and spatial resolution: Applied Geography, v. 93, p. 25-36, https://doi.org/10.1016/j.apgeog.2018.02.005.
Instructions for understanding and running InVEST are available here: http://data.naturalcapitalproject.org/nightly-build/invest-users-guide/html/
Purpose
While ES research has grown substantially in the last 10-15 years, assessments of how data and model choices influence estimates of ES are relatively fewer and newer. This issue is particularly important when ES assessments are conducted in developing countries, which may have limited data availability and modeling expertise. At least four types of data and model variability exist. In the paper, we address the first three of these modeling and data questions using a quantitative example from Rwanda.
