Publication Details |
| Category | Text Publication |
| Reference Category | Journals |
| DOI | 10.1016/j.envsoft.2021.104982 |
| Title (Primary) | Mapping water ecosystem services: Evaluating InVEST model predictions in data scarce regions |
| Author | Benra, F.; de Frutos, A.; Gaglio, M.; Álvarez-Garretón, C.; Felipe-Lucia, M.R.; Bonn, A.
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| Source Titel | Environmental Modelling & Software |
| Year | 2021 |
| Department | iDiv; ESS |
| Volume | 138 |
| Page From | art. 104982 |
| Language | englisch |
| Topic | T5 Future Landscapes |
| Data and Software links | http://dx.doi.org/10.17632/swxzspm4f3.1 |
| Keywords | ecosystem service model; water regulation; water supply; South America; data scarce regions; blue ecosystem services |
| Abstract | Sustainable management of water ecosystem services requires reliable information to support decision making. We evaluate the performance of the InVEST Seasonal Water Yield Model (SWYM) against water monitoring records in 224 catchments in southern Chile. We run the SWYM in three years (1998, 2007 and 2013) to account for recent land-use change and climatic variations. We computed squared Pearson correlations between SWYM monthly quickflow predictions and streamflow observations and applied a generalized mixed‐effects model to evaluate annual estimations. Results show relatively low monthly correlations with marked latitudinal and temporal variations while annual estimates show a good match between observed and modelled values, especially for values under 1000 mm/year. Better predictions were observed in regions with high rainfall and in dry years while poorer predictions were found in snow dominated and drier regions. Our results improve SWYM performance and contribute to water supply and regulation decision-making, particularly in data scarce regions. |
| Persistent UFZ Identifier | https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=24235 |
| Benra, F., de Frutos, A., Gaglio, M., Álvarez-Garretón, C., Felipe-Lucia, M.R., Bonn, A. (2021): Mapping water ecosystem services: Evaluating InVEST model predictions in data scarce regions Environ. Modell. Softw. 138 , art. 104982 10.1016/j.envsoft.2021.104982 |
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