Seasonal water yield modelling of the Baidrag River Basin
DOI:
https://doi.org/10.5564/mjgg.v62i46.4138Keywords:
Quickflow, Baseflow, Evapotranspiration, Ecosystem services, InVEST-SWY modelAbstract
Water yield plays a critical role in sustaining ecological balance and supporting ecosystem services, yet it exhibits significant spatial and seasonal variability. Estimating water yield in data-scarce regions remains challenging, emphasizing the importance of model-based approaches. This study evaluated the performance of the Integrated Valuation of Ecosystem Services and Tradeoffs - Seasonal Water Yield Model (InVEST-SWYM) for simulating seasonal water yield in the Baidrag River Basin, Mongolia, over the period 2000–2020. The model was applied using key input parameters, including monthly precipitation, evapotranspiration, a digital elevation model, land use and land cover data, and soil characteristics derived from satellite imagery and primary sources. Key outputs included monthly and annual quickflow (QF), baseflow (B), and actual evapotranspiration (AET). Results revealed increasing trends in precipitation (180.9–253.7 mm/year) and quickflow (15.15-21.77 mm/year), with peak runoff in July. AET increased from 163.4 mm to 230.14 mm, while potential evapotranspiration (PET) declined from 1314.9 mm to 1139.6 mm. Baseflow remained low (0.1–4 mm), with higher values in northern and north-eastern zones. Quickflow showed strong seasonality and was spatially concentrated in the northern and western sub-basins. These patterns were interpreted to reflect the combined influence of precipitation distribution, topographic gradients, and land cover characteristics, based on visual analysis of spatial model outputs. The results highlight reduced flows during winter due to frozen ground and elevated summer flows linked to precipitation peaks. The seasonal quickflow estimation was validated by comparing the predicted results with observed streamflow data from the Baidrag-Baidrag gauging station for the years 2000 and 2020. To assess statistical correlation and reliability, Nash–Sutcliffe Efficiency (NSE) and Percent Bias (PBIAS) were calculated. NSE values were 0.77 and 0.79 for 2000 and 2020, respectively, with PBIAS values of 25.64 and –23.64. The model tended to overestimate streamflow during spring snowmelt (May) and underestimate it during the summer rainfall season, likely due to bias in CHIRPS precipitation data.
Downloads
87
References
[1] B. Burkhard, F. Kroll, S. Nedkov, and F. Müller, “Mapping ecosystem service supply, demand and budgets,” Ecological indicators, vol. 21, pp. 17–29, 2012. Available: doi: 10.1016/j.ecolind.2011.06.019.
[2] R. O. TEEB, “Mainstreaming the Economics of Nature,” 2010. ISBN: 978-3-9813410-0-4.
[3] NRCS (Natural Resources Conservation Service), “National engineering handbook,” 2009.
[4] S. Halder, S. Das, and S. Basu, “Estimation of seasonal water yield using InVEST model: a case study from West Bengal, India,” Arabian Journal of Geosciences, vol. 15, no. 14, pp. 1–18, 2022. Available: doi: 10.1007/s12517-022-10551-2.
[5] H. Lu et al., “Spatiotemporal Water Yield Variations and Influencing Factors in the Lhasa River Basin, Tibetan Plateau,” Water, vol. 12, no. 5, 2020. Available: doi: 10.3390/w12051498.
[6] J. Vila-Traver, E. Aguilera, J. Infante-Amate, and M. González de Molina, “Climate change and industrialization as the main drivers of Spanish agriculture water stress,” Science of The Total Environment, vol. 760, pp. 143399, Mar. 2021. Available: doi: 10.1016/j.scitotenv.2020.143399.
[7] D. Molden, T. Oweis, P. Steduto, P. Bindraban, M. A. Hanjra, and J. Kijne, “Improving agricultural water productivity: Between optimism and caution,” Agricultural Water Management, vol. 97, no. 4, pp. 528–535, Apr. 2010. Available: doi: 10.1016/j.agwat.2009.03.023.
[8] D. dos R. Pereira, M. A. Martinez, F. F. Pruski, and D. D. da Silva, “Hydrological simulation in a basin of typical tropical climate and soil using the SWAT model part I: Calibration and validation tests,” Journal of Hydrology: Regional Studies, vol. 7, pp. 14–37, Sep. 2016. Available: doi: 10.1016/j.ejrh.2016.05.002.
[9] J. Norvanchig and T. O. Randhir, “Simulation of ecohydrological processes influencing water supplies in the Tuul River watershed of Mongolia,” Journal of Hydroinformatics, vol. 23, no. 5, pp. 1130–1145, 2021. Available: doi: 10.2166/hydro.2021.056
[10] B. Dorjsuren et al., “Study on relationship of land cover changes and ecohydrological processes of the Tuul River Basin,” Sustainability, vol. 13, no. 3, pp. 1153, 2021. Available: doi: 10.3390/su13031153.
[11] C. Sukhbaatar, R. U. Sajjad, J. Lunten, S. Yu, and C. Lee, “Climate change impact on the Tuul river flow in a Semiarid region in Mongolia,” Water Environment Research, vol. 89, no. 6, pp. 527–538, 2017. Available: doi: 10.2175/106143016X14798353399223.
[12] J. W. Redhead et al., “Empirical validation of the InVEST water yield ecosystem service model at a national scale,” Science of the Total Environment, vol. 569, pp. 1418–1426, 2016. Available: doi: 10.1016/j.scitotenv.2016.06.227.
[13] Institute of Geography and Geoecology, “Assessment of the Ecological capacity of the Baidrag river basin at the landscape level for further action to protect and use it properly,” Ulaanbaatar, UNDP-Consulting Service, 2022.
[14] G. Davaa, Ed., Surface water regime and resources of Mongolia. 2015.
[15] R. Sharp et al., “InVEST 3.7. 0 user guide. Collaborative publication by The Natural Capital Project,” 2019.
[16] A. Rauf and A. R. Ghumman, “Impact assessment of rainfall-runoff simulations on the flow duration curve of the Upper Indus River—A comparison of data-driven and hydrologic models,” Water, vol. 10, no. 7, pp. 876, 2018. Available: doi: 10.3390/w10070876.
[17] C. Funk, A. Verdin, J. Michaelsen, P. Peterson, D. Pedreros, and G. Husak, “A global satellite-assisted precipitation climatology,” Earth System Science Data, vol. 7, no. 2, pp. 275, 2015. Available: doi: 10.5194/essd-7-275-2015.
[18] S. Running, Q. Mu, M. Zhao, and A. Moreno, “MOD16A2GF MODIS/Terra Net Evapotranspiration Gap-Filled 8-Day L4 Global 500 m SIN Grid V006,” NASA EOSDIS Land Processes DAAC, 2019.
[19] A. D. Feldman, Hydrologic modeling system HEC-HMS: technical reference manual. US Army Corps of Engineers, Hydrologic Engineering Center, 2000.
[20] R. G. Allen, L. S. Pereira, D. Raes, and M. Smith, “Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56,” Fao, Rome, vol. 300, no. 9, p. D05109, 1998, ISBN: 92-5-104219-5.
[21] D. N. Moriasi, J. G. Arnold, M. W. Van Liew, R. L. Bingner, R. D. Harmel, and T. L. Veith, “Model evaluation guidelines for systematic quantification of accuracy in watershed simulations,” Transactions of the ASABE, vol. 50, no. 3, pp. 885–900, 2007. Available: doi: 10.13031/2013.23153.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Purevsuren Munkhtur, Batnyam Tseveengerel, Bayanjargal Bumtsend, Odbaatar Enkhjargal
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright on any research article in the Mongolian Journal of Geography and Geoecology is retained by the author(s).
The authors grant the Mongolian Journal of Geography and Geoecology a license to publish the article and identify itself as the original publisher.
Articles in the Mongolian Journal of Geography and Geoecology are Open Access articles published under a Creative Commons Attribution 4.0 International License CC BY.
This license permits use, distribution and reproduction in any medium, provided the original work is properly cited.
