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Uncertainty in data for hydrological ecosystem services modelling: Potential implications for estimating services and beneficiaries for the CAZ Madagascar

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  • van Soesbergen, Arnout
  • Mulligan, Mark

Abstract

This study assesses the differences in modelled total runoff and modelled runoff delivered to people between different rainfall and population datasets in the Ankeniheny Zhamena Corridor (CAZ) of Eastern Madagascar. Runoff is estimated using the WaterWorld hydrological model driven by six rainfall datasets, and population is derived from five population datasets. Model results for runoff under different rainfall datasets lead to variability in runoff (coefficient of variation) up to 99% for single months and 60% in the dry season. These differences are much larger than differences in estimated runoff between baseline and complete deforestation scenario for each rainfall dataset. Population estimates for the CAZ range from 1.2 to 2 million between the population datasets. Differences in runoff under different rainfall datasets lead to an average of 356,000 people estimated to receive 90% more runoff and nearly 750,000 people estimated to receive 50% more or less runoff relative to a baseline rainfall dataset. Therefore, the choice of rainfall data in hydrological ecosystem services modelling has a large influence on estimates of ecosystem service flows highlighting the need for modellers to justify their data choices and report on uncertainties in results, particularly in light of potential policy decisions based on modelled outcomes.

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  • van Soesbergen, Arnout & Mulligan, Mark, 2018. "Uncertainty in data for hydrological ecosystem services modelling: Potential implications for estimating services and beneficiaries for the CAZ Madagascar," Ecosystem Services, Elsevier, vol. 33(PB), pages 175-186.
    • Handle: RePEc:eee:ecoser:v:33:y:2018:i:pb:p:175-186
    DOI: 10.1016/j.ecoser.2018.08.005
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    1. Franz Schug & David Frantz & Sebastian van der Linden & Patrick Hostert, 2021. "Gridded population mapping for Germany based on building density, height and type from Earth Observation data using census disaggregation and bottom-up estimates," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-23, March.
    2. Ávila-García, Daniela & Morató, Jordi & Pérez-Maussán, Ana I. & Santillán-Carvantes, Patricia & Alvarado, Jannice & Comín, Francisco A., 2020. "Impacts of alternative land-use policies on water ecosystem services in the Río Grande de Comitán-Lagos de Montebello watershed, Mexico," Ecosystem Services, Elsevier, vol. 45(C).
    3. Hooftman, Danny A.P. & Bullock, James M. & Jones, Laurence & Eigenbrod, Felix & Barredo, José I. & Forrest, Matthew & Kindermann, Georg & Thomas, Amy & Willcock, Simon, 2022. "Reducing uncertainty in ecosystem service modelling through weighted ensembles," Ecosystem Services, Elsevier, vol. 53(C).
    4. Bryant, Benjamin P. & Borsuk, Mark E. & Hamel, Perrine & Oleson, Kirsten L.L. & Schulp, C.J.E. & Willcock, Simon, 2018. "Transparent and feasible uncertainty assessment adds value to applied ecosystem services modeling," Ecosystem Services, Elsevier, vol. 33(PB), pages 103-109.

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