Downscaling Seasonal Climate Forecasts for Risks Management of Rice Production in the Philippines
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Abstract Objectives: This study is centered on the potential use of a dynamic seasonal climate forecast for informing climate risk management in Central Luzon, Philippines to improve rice productivity and resilience. Specifically, we seek to test the downscalibility of the seasonal climate forecasts in the region using a multi-variate spatio-temporal downscaling technique, understand and assess the predictability of rice yield at selected growing areas in the Philippines, and provide guidance on how to develop agricultural risk management strategies. Methods/statistical analysis: The coupled Global Circulation Model (GCM) CFSv2 was used to evaluate the utility of MJJA (May-June-JulyAugust) rainfall forecasts for risk management of rice production in Central Luzon, Philippines. We used a non-homogeneous hidden Markov model (NHMM) to downscale and simulate the GCM forecasts to selected weather stations in the region. On the other hand, we evaluated the skill of the climate forecasts for predicting crop yields. The simulated rainfall was used to drive the rice models set up in DSSATv4.5. Other weather variables needed by DSSAT were generated and conditioned on the occurrence of rainfall based on NHMM rainfall simulation. Simulated rice yields obtained from these models using observed (i.e., simulated by observed weather) and conditioned rainfall (i.e., NHMM downscaling) serve as a baseline for evaluating yields. We also performed a sensitivity analysis to assess appropriate planting windows for the target season for risk management. Findings: Inter-annual variability of rainfall is moderately simulated, with a skill (r) of 0.41, suggesting that NHMM was fairly successful downscaling rainfall from the regional scale given the predictive nature of the predictor, at three months lead time. The observed MJJA mean rainfall from the six stations falls at around 56% within the interquartile ranges of simulated rainfall, which indicates reasonable skill of the NHMM downscaling CFSv2’s MJJA rainfall hindcasts. Simulated yield was found comparable with the observed at 5% level of significance using t-test (p > 0.05). The correlations between observed and predicted yields are equal to 0.56. This indicates that the models can represent about 31.36% of the inter-annual variability of the yields of rice, albeit of the three months lead-time of the CFSv2 hindcasts. It suggests a reasonable performance of the models in simulating rice yield using the NHMM generated climate information. Climatologically, the best planting and sowing windows for rice in the study area is on the first week of May. This can be adjusted by using seasonal climate forecasts information. Harvest period should not cross over in the month of September to avoid exposure to heavy typhoons. Application/ improvements: Sensitivity analysis showed that planting rice earlier than the usual planting windows practiced by the farmers could improve resilience to climate risks. Managing the variance of this management window, however, is of paramount importance, which can be informed by skillful climate forecasts. Keywords: Seasonal Climate Forecast, Downscaling, Nonhomogenous Hidden Markov Model, Crop Model, Climate Risks Management, Yield Prediction.
摘要 研究目标：本研究聚焦于将动态季节气候预报应用于菲律宾吕宋岛中部的气候风险管理，以提升水稻生产能力与气候韧性。具体而言，本研究旨在采用多变量时空降尺度技术，检验该区域季节气候预报的降尺度性能；解析并评估菲律宾选定种植区的水稻产量可预报性；同时为制定农业风险管理策略提供指导。 方法与统计分析：本研究采用耦合全球环流模型（Global Circulation Model, GCM）CFSv2，评估MJJA（5月-6月-7月-8月）降雨预报在菲律宾吕宋岛中部水稻生产风险管理中的应用价值。本研究使用非齐次隐马尔可夫模型（Non-homogeneous Hidden Markov Model, NHMM），对该区域选定气象站点的GCM预报结果进行降尺度与模拟。另一方面，本研究评估了气候预报用于作物产量预测的预报技巧。模拟降雨数据被用于驱动基于DSSATv4.5构建的水稻模型；DSSAT所需的其他气象变量则基于NHMM的降雨模拟结果，结合降雨发生条件进行生成与校准。本研究分别使用观测降雨（即由观测气象数据模拟得到）与经NHMM降尺度得到的条件化降雨，运行上述模型得到模拟水稻产量，以此作为产量评估的基准。此外，本研究开展了敏感性分析，以确定风险管理目标季的适宜播种窗口期。 研究结果：降雨的年际变率得到了中等程度的模拟，预报技巧（相关系数r）为0.41，这意味着在3个月预报提前期下，基于预报因子的预测特性，NHMM在区域尺度降雨降尺度方面取得了较为理想的效果。六个气象站点的观测MJJA平均降雨量中，约56%落在模拟降雨的四分位距范围内，这表明NHMM对CFSv2的MJJA降雨回报场进行降尺度的效果较为合理。通过t检验分析可知，在5%显著性水平下，模拟产量与观测产量无显著差异（p>0.05）。观测产量与预测产量的相关系数为0.56，这表明尽管CFSv2的降雨回报存在3个月的预报提前期，但该模型仍可解释约31.36%的水稻产量年际变率，证明基于NHMM生成的气候信息进行水稻产量模拟的模型表现较为合理。从气候学角度来看，本研究区域水稻的最佳播种窗口期为5月第一周，该窗口可通过季节气候预报信息进行调整；同时，收获期应避免跨越9月，以规避强台风带来的风险。 应用与优化方向：敏感性分析结果显示，相较于农户常规播种期提前种植水稻，可提升其应对气候风险的韧性。不过，调控该种植窗口的离散度至关重要，而这可通过精准的气候预报信息实现。 关键词：季节气候预报、降尺度、非齐次隐马尔可夫模型、作物模型、气候风险管理、产量预测。
