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JDR Vol.21 No.2 pp. 299-313
(2026)

Paper:

A Climate–Hydro–Crop Modeling Framework for Assessing Climate Change Impacts on Hydrometeorology and Rain-Fed Paddy Yield in the Pampanga River Basin, Philippines

Mohamed Rasmy*,†, Koki Homma** ORCID Icon, Tomoki Ushiyama* ORCID Icon, Ralph Allen Acierto* ORCID Icon, Miho Ohara*, Yoshito Kikumori*, and Toshio Koike*

*International Centre for Water Hazard and Risk Management (ICHARM), Public Works Research Institute (PWRI)
1-6 Minamihara, Tsukuba, Ibaraki 305-8516, Japan

Corresponding author

**Graduate School of Agricultural Science, Tohoku University
Sendai, Japan

Received:
October 17, 2025
Accepted:
February 25, 2026
Published:
April 1, 2026
Keywords:
climate change, flood, drought, rice yields, climate–hydro–crop modeling
Abstract

Climate change poses a significant threat to water and food security, necessitating an urgent need to quantify the evolving climate-related risks within the water-food nexus. Despite extensive research on climate change impacts on hydrometeorology, the climate–hydro–crop nexus remains underexplored. This study integrated downscaled and bias-corrected MRI-AGCM-3.2S climate outputs, a seamless hydrological model, and a rain-fed paddy model to simulate the climate change impacts on hydrological extremes, water availability, and rain-fed rice yield in the Pampanga River basin under the RCP8.5 scenario. The results showed a marginal increase in annual rainfall, accompanied by greater temporal and spatial variability. Extreme rainfall and associated flood events were projected to be more frequent and intensified. Despite the increase in basin-averaged annual rainfall, the annual discharge at San Isidro was projected to decrease due to weakening post-monsoon rainfall. The projected basin-averaged rain-fed rice yield showed a modest decrease at the basin scale, with larger losses in the eastern region due to water stress and in low-lying areas due to flooding compared to the past climate. These findings highlight the growing hydrological and agricultural risks posed by climate change in the basin and thus require regional climate-adaptive and climate-resilient strategies to safeguard water-food security. Future research will include intensive data collection, a comprehensive sensitivity assessment, and the integration of irrigation practices and the water storage function.

Cite this article as:
M. Rasmy, K. Homma, T. Ushiyama, R. Acierto, M. Ohara, Y. Kikumori, and T. Koike, “A Climate–Hydro–Crop Modeling Framework for Assessing Climate Change Impacts on Hydrometeorology and Rain-Fed Paddy Yield in the Pampanga River Basin, Philippines,” J. Disaster Res., Vol.21 No.2, pp. 299-313, 2026.
Data files:
References
  1. [1] Intergovernmental Panel on Climate Change (IPCC), “Climate Change 2022 – Impacts, Adaptation, and Vulnerability: Working Group II Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change,” Cambridge University Press. 2023. https://doi.org/10.1017/9781009325844
  2. [2] A. Zhang, W. Liu, Z. Yin, G. Fu, and C. Zheng, “How will climate change affect the water availability in the Heihe River basin, northwest China?,” J. Hydrometeorol., Vol.17, No.5, pp. 1517-1542, 2016. https://doi.org/10.1175/JHM-D-15-0058.1
  3. [3] Q. Sun, C. Miao, and Q. Duan, “Comparative analysis of CMIP3 and CMIP5 global climate models for simulating the daily mean, maximum, and minimum temperatures and daily precipitation over China,” J. Geophys. Res. Atmos., Vol.120, No.10, pp. 4806-4824, 2015. https://doi.org/10.1002/2014JD022994
  4. [4] S. Kusunoki and O. Arakawa, “Are CMIP5 models better than CMIP3 models in simulating precipitation over East Asia?,” J. Clim., Vol.28, No.14, pp. 5601-5621, 2015. https://doi.org/10.1175/JCLI-D-14-00585.1
  5. [5] B. Rockel, “The regional downscaling approach: A brief history and recent advances,” Curr. Clim. Change Rep., Vol.1, No.1, pp. 22-29, 2015. https://doi.org/10.1007/s40641-014-0001-3
  6. [6] E. P. Diaconescu and R. Laprise, “Can added value be expected in RCM-simulated large scales?,” Clim. Dyn., Vol.41, No.7, pp. 1769-1800, 2013. https://doi.org/10.1007/s00382-012-1649-9
  7. [7] P. Hui et al., “Impact of resolution on regional climate modeling in the source region of Yellow River with complex terrain using RegCM3,” Theor. Appl. Climatol., Vol.125, No.1, pp. 365-380, 2016. https://doi.org/10.1007/s00704-015-1514-y
  8. [8] L. Wang et al., “Development of a distributed biosphere hydrological model and its evaluation with the Southern Great Plains Experiments (SGP97 and SGP99),” J. Geophys. Res. Atmos., Vol.114, No.D8, Article No.D08107, 2009. https://doi.org/10.1029/2008JD010800
  9. [9] T. Sayama, G. Ozawa, T. Kawakami, S. Nabesaka, and K. Fukami, “Rainfall–runoff–inundation analysis of the 2010 Pakistan flood in the Kabul River basin,” Hydrol. Sci. J., Vol.57, No.2, pp. 298-312, 2012. https://doi.org/10.1080/02626667.2011.644245
  10. [10] Y. Zheng, J. Li, T. Zhang, Y. Rong, and P. Feng, “Exploring the application of flood scaling property in hydrological model calibration,” J. Hydrometeorol., Vol.22, No.12, pp. 3255-3274, 2021. https://doi.org/10.1175/JHM-D-21-0123.1
  11. [11] M. Rasmy, T. Sayama, and T. Koike, “Development of Water and Energy Budget-based Rainfall-Runoff-Inundation model (WEB-RRI) and its verification in the Kalu and Mundeni River Basins, Sri Lanka,” J. Hydrol., Vol.579, Article No.124163, 2019. https://doi.org/10.1016/j.jhydrol.2019.124163
  12. [12] B. A. M. Bouman, T. P. Tuong, M. J. Kropff, and H. H. van Laar, “The model ORYZA2000 to simulate growth and development of lowland rice,” MODSIM 2001: Int. Conf. on Modelling and Simulation, pp. 1793-1798, 2001.
  13. [13] J. W. Jones et al., “Decision support system for agrotechnology transfer: DSSAT v3,” G. Y. Tsuji, G. Hoogenboom, and P. K. Thornton (Eds.), “Understanding options for agricultural production,” pp. 157-177, Springer, 1998. https://doi.org/10.1007/978-94-017-3624-4_8
  14. [14] T. Horie, H. Nakagawa, H. G. S. Centeno, and M. J. Kropff, “The rice crop simulation model SIMRIW and its testing,” R. B. Matthews, M. J. Kropff, D. Bachelet, and H. H. van Laar (Eds.), “Modeling the Impact of Climate Change on Rice Production in Asia,” pp. 51-66, CAB International, 1995.
  15. [15] K. Homma, M. Maki, and Y. Hirooka, “Development of a rice simulation model for remote-sensing (SIMRIW-RS),” J. Agric. Meteorol., Vol.73, No.1, pp. 9-15, 2017. https://doi.org/10.2480/agrmet.d-14-00022
  16. [16] K. Homma and T. Horie, “The present situation and the future improvement of fertilizer applications by farmers in rainfed rice culture in northeast Thailand,” L. R. Elsworth and W. O. Paley (Eds.), “Fertilizers: Properties, Applications and Effects,” pp. 147-180, Nova Science Publishers, Inc., 2009.
  17. [17] M. Ohnishi, T. Horie, and Y. Koroda, “Simulating rice leaf area development and dry matter production in relation to plant N and weather,” Applications of Systems Approaches at the Field Level (Proc. of the 2nd Int. Symp. on Systems Approaches for Agricultural Development, Vol.2), pp. 271-284, 1997. https://doi.org/10.1007/978-94-017-0754-1_19
  18. [18] M. Maki, K. Sekiguchi, K. Homma, Y. Hirooka, and K. Oki, “Estimation of rice yield by SIMRIW-RS, a model that integrates remote sensing data into a crop growth model,” J. Agric. Meteorol., Vol.73, No.1, pp. 2-8, 2017. https://doi.org/10.2480/agrmet.D-14-00023
  19. [19] M. Raksapatcharawong, W. Veerakachen, K. Homma, M. Maki, and K. Oki, “Satellite-based drought impact assessment on rice yield in Thailand with SIMRIW-RS,” Remote Sens., Vol.12, No.13, Article No.2099, 2020. https://doi.org/10.3390/rs12132099
  20. [20] B. B. Shrestha, T. Okazumi, M. Miyamoto, and H. Sawano, “Flood damage assessment in the Pampanga River basin of the Philippines,” J. Flood Risk Manag., Vol.9, No.4, pp. 355-369, 2016. https://doi.org/10.1111/jfr3.12174
  21. [21] R. Mizuta et al., “Climate simulations using MRI-AGCM3.2 with 20-km grid,” J. Meteorol. Soc. Jpn., Vol.90A, pp. 233-258, 2012. https://doi.org/10.2151/jmsj.2012-A12
  22. [22] W. C. Skamarock et al., “A description of the advanced research WRF Version 3,” NCAR Technical Note, NCAR/TN-475+STR, 2008.
  23. [23] M. Nakanishi and H. Niino, “An improved Mellor–Yamada Level-3 model with condensation physics: Its design and verification,” Bound.-Layer Meteorol., Vol.112, No.1, pp. 1-31, 2004. https://doi.org/10.1023/B:BOUN.0000020164.04146.98
  24. [24] M. B. Ek et al., “Implementations of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model,” J. Geophys. Res. Atmos., Vol.108, No.D22, Article No.8851, 2003. https://doi.org/10.1029/2002JD003296
  25. [25] H. Inomata, K. Takeuchi, and K. Fukami, “Development of a statistical bias correction method for daily precipitation data of GCM20,” J. Jpn. Soc. Civ. Eng. Ser. B1 (Hydraul. Eng.), Vol.67, No.4, pp. I_247-I_252, 2011.
  26. [26] T. Horie, M. Yajima, and H. Nakagawa, “Yield forecasting,” Agric. Syst., Vol.40, Nos.1-3, pp. 211-236. 1992. https://doi.org/10.1016/0308-521x(92)90022-g
  27. [27] S. Kobayashi et al., “The JRA-55 reanalysis: General specifications and basic characteristics,” J. Meteorol. Soc. Jpn Ser. II, Vol.93, No.1, pp. 5-48, 2015. https://doi.org/10.2151/jmsj.2015-001

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Last updated on Apr. 22, 2026