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A multi-state Markov chain model to assess drought risks in rainfed agriculture: a case study in the Nineveh Plains of Northern Iraq
Stochastic Environmental Research and Risk Assessment ( IF 4.2 ) Pub Date : 2021-03-04 , DOI: 10.1007/s00477-021-01991-5
Rasha M. Fadhil , Koichi Unami

The occurrence of prolonged dry spells and the shortage of precipitation are two different hazardous factors affecting rainfed agriculture. This study investigates a multi-state Markov chain model with the states of dry spell length coupled with a probability distribution of positive rainfall depths. The Nineveh Plains of Northern Iraq is chosen as the study site, where the rainfed farmers are inevitably exposed to drought risks, for demonstration of applicability to real-time drought risk assessment. The model is operated on historical data of daily rainfall depths observed at the city Mosul bordering the Nineveh Plains during the period 1975–2018. The methodology is developed in the context of contemporary probability theory. Firstly, the Kolmogorov–Smirnov tests are applied to extracting two sub-periods where the positive rainfall depths obey to respective distinct gamma distributions. Then, empirical estimation of transition probabilities determining a multi-state Markov chain results in spurious oscillations, which are regularized in the minimizing total variation flow solving a singular diffusion equation with a degenerating coefficient that controls extreme values of 0 and 1. Finally, the model yields the statistical moments of the dry spell length in the future and the total rainfall depth until a specified terminal day. Those statistical moments, termed hazard futures, can quantify drought risks based on the information of the dry spell length up to the current day. The newly defined hazard futures are utilized to explore measures to avert drought risks intensifying these decades, aiming to establish sustainable rainfed agriculture in the Nineveh Plains.



中文翻译:

评估雨养农业干旱风险的多状态马尔可夫链模型:以伊拉克北部尼尼微平原为例

长期干旱和降雨不足是影响雨养农业的两个不同的危险因素。这项研究调查了一个多状态马尔可夫链模型,该模型具有干燥符咒长度状态以及正降雨深度的概率分布。选择伊拉克北部的尼尼微平原作为研究地点,在那里,雨养农户不可避免地面临干旱风险,以证明适用于实时干旱风险评估。该模型是根据1975-2018年期间与尼尼微平原接壤的摩苏尔市每日降雨深度的历史数据进行操作的。该方法是在当代概率论的背景下开发的。首先,Kolmogorov–Smirnov试验用于提取两个子时段,其中正降雨深度服从各自不同的伽马分布。然后,对确定多状态马尔可夫链的跃迁概率进行经验估计会导致杂散振荡,这些杂散振荡会在最小化总变化流的过程中得到正则化,从而求解具有退化系数的奇异扩散方程,该退化系数控制着0和1的极值。得出将来的干法术长度和总降雨深度的统计时刻,直到指定的终止日为止。这些统计时刻(称为“危险期货”)​​可以根据直到今天的干旱时间长度信息来量化干旱风险。

更新日期:2021-03-04
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