HomeRecoletos Multidisciplinary Research Journalvol. 13 no. 1 (2025)

Quantifying Ecosystem Disservice in the Philippines through Water Release Potential Estimation Using ES-Based Model: The Case of Balanac Watershed

Gemmalyn Trespalacio | Annadel Sapugay | Nico Jayson Anastacio | J. Carl Ureta | Joan Ureta

Discipline: environmental sciences

 

Abstract:

Background: Rapid land use change and intensified climate change impacts have altered a landscape's natural hydrological processes and ecosystem services. These changes may cause flooding, water quality degradation, and water scarcity. Methods: This study used the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Water Yield model to estimate the potential amount of water released by varying land cover in the Balanac Watershed, Philippines. The model calculated the change in the amount of water that is being released as surface runoff from different land cover types across spatial units of the landscape. Results: Findings showed that vegetated areas had the lowest water yield estimate, while built areas had the highest due to increased surface runoff. Conclusion: The study offers valuable information, particularly regarding the relative differences in water releases across various land cover types. It contributes to the limited knowledge of ecosystem service-based modeling in the Philippines.



References:

  1. Annual water yield: InVEST user guide. (n.d.). The Natural Capital Project. Retrieved October 19, 2023 from http://releases.naturalcapitalproject.org/investuserguide/latest/en/annual_water_yield.html
  2. Belete, M., Deng, J., Wang, K., Zhou, M., Zhu, E., Shifaw, E., & Bayissa, Y. (2020). Evaluation of satellite rainfall products for modeling water yield over the source Region of Blue Nile Basin. Science of the Total Environment, 708, 134834. https://doi.org/10.1016/j.scitotenv.2019.134834
  3. Benra, F., De Frutos, A., Gaglio, M., Álvarez-Garretón, C., Felipe-Lucia, M., & Bonn, A. (2021). Mapping water ecosystem services: Evaluating InVEST model predictions in data scarce regions. Environmental Modelling & Software, 138, 104982. https://doi.org/10.1016/j.envsoft.2021.104982
  4. Birkinshaw, S. J., O’Donnell, G., Glenis, V., & Kilsby, C. (2021). Improved hydrological modelling of urban catchments using runoff coefficients. Journal of Hydrology, 594, 125884. https://doi.org/10.1016/j.jhydrol.2020.125884
  5. Bouguerra, S., Stiti, B., Khalfaoui, M., Jebari, S., Khaldi, A., & Berndtsson, R. (2024). Modeling ecosystem regulation services and performing cost–benefit analysis for climate change mitigation through nature-based solutions using inVEST models. Sustainability, 16(16), 7201–7201. https://doi.org/10.3390/su16167201
  6. CGIAR-CSI. (2019, January 24). Global aridity index and potential evapotranspiration climate database v2. CGIAR-CSI. https://csidotinfo.wordpress.com/2019/01/24/global-aridity-index-and-potential-evapotranspiration-climate-database-v2/
  7. Cong, W., Sun, X., Guo, H., & Shan, R. (2020). Comparison of the SWAT and InVEST models to determine hydrological ecosystem service spatial patterns, priorities and trade-offs in a complex basin. Ecological Indicators, 112, 106089. https://doi.org/10.1016/j.ecolind.2020.106089
  8. Daneshi, A., Brouwer, R., Najafinejad, A., Panahi, M., Zarandian, A., & Maghsood, F. F. (2021). Modelling the impacts of climate and land use change on water security in a semi-arid forested watershed using InVEST. Journal of Hydrology, 593, 125621. https://doi.org/10.1016/j.jhydrol.2020.125621
  9. Döhren, P., & Haase, D. (2022). Geospatial assessment of urban ecosystem disservices: An example of poisonous urban trees in Berlin, Germany. Urban Forestry & Urban Greening, 67, 127440. https://doi.org/10.1016/j.ufug.2021.127440
  10. Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37(12), 4302–4315. https://doi.org/10.1002/joc.5086
  11. Fischer, G., Nachtergaele, F., Prieler, S., Van Velthuizen, H. T., Verelst, L., & Wiberg, D. (2008). Global agro-ecological zones assessment for agriculture (GAEZ 2008). International Institute for Applied Systems Analysis.
  12. Guo, M., Ma, S., Wang, L.-J., & Lin, C. (2021). Impacts of future climate change and different management scenarios on water-related ecosystem services: A case study in the Jianghuai ecological economic Zone, China. Ecological Indicators, 127, 107732. https://doi.org/10.1016/j.ecolind.2021.107732
  13. Guo, Q., Yu, C., Xu, Z., Yang, Y., & Wang, X. (2023). Impacts of climate and land-use changes on water yields: Similarities and differences among typical watersheds distributed throughout China. Journal of Hydrology: Regional Studies, 45, 101294. https://doi.org/10.1016/j.ejrh.2022.101294
  14. Hu, Y., Gao, M., & Batunacun. (2020). Evaluations of water yield and soil erosion in the Shaanxi-Gansu Loess Plateau under different land use and climate change scenarios. Environmental Development, 34, 100488. https://doi.org/10.1016/j.envdev.2019.100488
  15. Iizuka, K., Johnson, B. A., Onishi, A., Magcale-Macandog, D. B., Endo, I., & Bragais, M. (2017). Modeling future urban sprawl and landscape change in the Laguna de Bay Area, Philippines. Land, 6(2), 26. https://doi.org/10.3390/land6020026
  16. Im, J. (2019). Green Streets to Serve Urban Sustainability: Benefits and Typology. Sustainability, 11(22), 6483. https://doi.org/10.3390/su11226483
  17. Karra, K., Kontgis, C., Statman-Weil, Z., Mazzariello, J. C., Mathis, M., & Brumby, S. P. (2021). Global land use / land cover with sentinel 2 and deep learning. 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 4704–4707. https://doi.org/10.1109/IGARSS47720.2021.9553499
  18. Lang, Y., Song, W., & Zhang, Y. (2017). Responses of the water-yield ecosystem service to climate and land use change in Sancha River Basin, China. Physics and Chemistry of the Earth, Parts A/B/C, 101, 102–111. https://doi.org/10.1016/j.pce.2017.06.003
  19. Mallari, N. A., Rosales, R. M., Castillo, G., Angeles, M. D., Francisco, H., Orbeta, E., Predo, C., Arcenas, A., Balangue, T., Lasmarias, N., Coroza, O., Masigan, J. P., Bautista, M. A., Edaño, J. W., Jimenez, J. P., Palermo, F., Parr, R. A., Shiraishi, J., Tee, C. K., & Uy, Q. A. (2024). Sukat ng kalikasan. Department of Environment and Natural Resources.
  20. Paringit, E. C., & Abucay, E. R. (2017). LiDAR surveys and flood mapping of Sta. Cruz. UP Training Center for Applied Geodesy and Photogrammetry (TCAGP). https://dream.upd.edu.ph/assets/Publications/LiDAR-Technical-Reports/UPLB/LiDAR-Surveys-and-Flood-Mapping-of-Sta.-Cruz-River.pdf
  21. Pei, H., Liu, M., Shen, Y., Xu, K., Zhang, H., Li, Y., & Luo, J. (2022). Quantifying impacts of climate dynamics and land-use changes on water yield service in the agro-pastoral ecotone of northern China. Science of the Total Environment, 809, 151153–151153. https://doi.org/10.1016/j.scitotenv.2021.151153
  22. Philippine Statistics Authority. (2021). Population and housing statistics. https://psa.gov.ph/statistics/population-and-housing/node/164786
  23. Redhead, J. W., Stratford, C., Sharps, K., Jones, L., Ziv, G., Clarke, D., Oliver, T. H., & Bullock, J. M. (2016). Empirical validation of the InVEST water yield ecosystem service model at a national scale. Science of the Total Environment, 569-570, 1418–1426. https://doi.org/10.1016/j.scitotenv.2016.06.227
  24. Schilling, K. E., Jha, M. K., Zhang, Y.-K., Gassman, P. W., & Wolter, C. F. (2008). Impact of land use and land cover change on the water balance of a large agricultural watershed: Historical effects and future directions. Water Resources Research, 44(7). https://doi.org/10.1029/2007wr006644
  25. Sharma, S. (2017). Effects of urbanization on water resources–facts and figures. International Journal of Scientific and Engineering Research, 8(4), 433–459.
  26. Sinasson S, K. G., Shackleton, C. M., Ruwanza, S., & Thondhlana, G. (2024). Contextual and socio-economic factors affected urban dwellers experiences of and vulnerability to ecosystem disservices. Scientific African, 26, e02404–e02404. https://doi.org/10.1016/j.sciaf.2024.e02404
  27. Sit, K. Y., Ng, K. Y., & Zhang, H. (2024). Understanding typhoon-induced vegetation loss and potential ecosystem disservices from land use zonings perspective in high-density Hong Kong. Applied Geography, 170, 103345–103345. https://doi.org/10.1016/j.apgeog.2024.103345
  28. Sparkman, S. A., Hogan, D. M., Hopkins, K. G., & Loperfido, J. V. (2017). Modeling watershed-scale impacts of stormwater management with traditional versus low impact development design. Journal of the American Water Resources Association, 53(5), 1081–1094. https://doi.org/10.1111/1752-1688.12559
  29. Tong, S. T. Y., Sun, Y., Ranatunga, T., He, J., & Yang, Y. J. (2012). Predicting plausible impacts of sets of climate and land use change scenarios on water resources. Applied Geography, 32(2), 477–489. https://doi.org/10.1016/j.apgeog.2011.06.014
  30. Tundu, C., Tumbare, M. J., & Kileshye Onema, J.-M. (2018). Sedimentation and its impacts/effects on river system and reservoir water quality: Case study of Mazowe catchment, Zimbabwe. Proceedings of the International Association of Hydrological Sciences, 377, 57–66. https://doi.org/10.5194/piahs-377-57-2018
  31. Ureta, J. C., Trespalacio, G., Anastacio, N. J., Sapugay, A., & Ureta, J. (2022). Estimating sediment export and retention capacity of existing land cover in Balanac and Sta. Cruz watersheds, Philippines using InVEST-SDR model. Philippine Journal of Science, 151(5), 1963–1978. https://doi.org/10.56899/151.05.34
  32. Vigerstol, K. L., & Aukema, J. E. (2011). A comparison of tools for modeling freshwater ecosystem services. Journal of Environmental Management, 92(10), 2403–2409. https://doi.org/10.1016/j.jenvman.2011.06.040
  33. von Döhren, P., & Haase, D. (2022). Geospatial assessment of urban ecosystem disservices: An example of poisonous urban trees in Berlin, Germany. Urban Forestry & Urban Greening, 67, 127440. https://doi.org/10.1016/j.ufug.2021.127440
  34. Wohlfart, C., Mack, B., Liu, G., & Kuenzer, C. (2017). Multi-faceted land cover and land use change analyses in the Yellow River Basin based on dense Landsat time series: Exemplary analysis in mining, agriculture, forest, and urban areas. Applied Geography, 85, 73–88. https://doi.org/10.1016/j.apgeog.2017.06.004
  35. Wu, F., Zhan, J., Chen, J., He, C., & Zhang, Q. (2015). Water yield variation due to forestry change in the head-water area of Heihe River Basin, Northwest China. Advances in Meteorology, 2015, 1–8. https://doi.org/10.1155/2015/786764
  36. Yang, D. H., Liu, B., Tang, L., Chen, L., Li, X., & Xu, X. (2019). Estimation of water provision service for monsoon catchments of South China: Applicability of the InVEST model. Landscape and Urban Planning, 182, 133–143. https://doi.org/10.1016/j.landurbplan.2018.10.011.
  37. Yifru, B. A., Chung, I.-M., Kim, M.-G., & Chang, S. W. (2021). Assessing the effect of land/use land cover and climate change on water yield and groundwater recharge in East African rift valley using integrated model. Journal of Hydrology: Regional Studies, 37, 100926. https://doi.org/10.1016/j.ejrh.2021.100926