Mapping water ecosystem services: Evaluating InVEST model predictions in data scarce regions

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Mapping water ecosystem services: Evaluating InVEST model predictions in data scarce regions. / Benra, Felipe; De Frutos, Angel; Gaglio, M. et al.

in: Environmental Modelling and Software, Jahrgang 138, 104982, 01.04.2021.

Publikation: Beiträge in ZeitschriftenZeitschriftenaufsätzeForschungbegutachtet

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Benra F, De Frutos A, Gaglio M, Álvarez-Garretón C, Felipe-Lucia MR, Bonn A. Mapping water ecosystem services: Evaluating InVEST model predictions in data scarce regions. Environmental Modelling and Software. 2021 Apr 1;138:104982. doi: 10.1016/j.envsoft.2021.104982

Bibtex

@article{88a938abb9c645d58ffcb21e92f129e0,
title = "Mapping water ecosystem services: Evaluating InVEST model predictions in data scarce regions",
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 modeled 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.",
keywords = "Ecosystems Research, Ecosystem service model, Water regulation, Water supply, South America, Data scarce regions, Blue ecosystem services",
author = "Felipe Benra and {De Frutos}, Angel and M. Gaglio and C. {\'A}lvarez-Garret{\'o}n and Felipe-Lucia, {Mar{\'i}a R.} and Aletta Bonn",
note = "Publisher Copyright: {\textcopyright} 2021 Elsevier Ltd",
year = "2021",
month = apr,
day = "1",
doi = "10.1016/j.envsoft.2021.104982",
language = "English",
volume = "138",
journal = "Environmental Modelling and Software",
issn = "1364-8152",
publisher = "Pergamon Press",

}

RIS

TY - JOUR

T1 - Mapping water ecosystem services: Evaluating InVEST model predictions in data scarce regions

AU - Benra, Felipe

AU - De Frutos, Angel

AU - Gaglio, M.

AU - Álvarez-Garretón, C.

AU - Felipe-Lucia, María R.

AU - Bonn, Aletta

N1 - Publisher Copyright: © 2021 Elsevier Ltd

PY - 2021/4/1

Y1 - 2021/4/1

N2 - 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 modeled 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.

AB - 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 modeled 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.

KW - Ecosystems Research

KW - Ecosystem service model

KW - Water regulation

KW - Water supply

KW - South America

KW - Data scarce regions

KW - Blue ecosystem services

UR - http://www.scopus.com/inward/record.url?scp=85100680839&partnerID=8YFLogxK

U2 - 10.1016/j.envsoft.2021.104982

DO - 10.1016/j.envsoft.2021.104982

M3 - Journal articles

VL - 138

JO - Environmental Modelling and Software

JF - Environmental Modelling and Software

SN - 1364-8152

M1 - 104982

ER -

DOI