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**
, Tomoki Ushiyama*
, Ralph Allen Acierto*
, 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
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.
- [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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] W. C. Skamarock et al., “A description of the advanced research WRF Version 3,” NCAR Technical Note, NCAR/TN-475+STR, 2008.
- [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] 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] 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] 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] 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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