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Agricultural drought periods analysis by using nonhomogeneous poisson models and regionalization of appropriate model parameters
Tellus A: Dynamic Meteorology and Oceanography ( IF 2.247 ) Pub Date : 2021-07-29 , DOI: 10.1080/16000870.2021.1948241
Asad Ellahi 1 , Ijaz Hussain 1 , Muhammad Zaffar Hashmi 2 , Mohammed Mohammed Ahmed Almazah 3, 4 , Fuad S. Al-Duais 5, 6
Affiliation  

Abstract

Precipitation has a dominant role in agriculture, and a regular rain pattern is usually vital to agriculture; excessive or inadequate rainfall can be harmful. In this paper, an agricultural drought index is utilized to study the agricultural drought periods and analyze them with their intensities at various locations. Some nonhomogeneous Poisson models are also used to calculate the probability of agricultural droughts (number of times occurred) in a time interval of interest. It is to be assumed that the number of agricultural drought event occurrences is a Nonhomogeneous Poisson Process (NHPP) has a rate function, which depends on some parameters that must be estimated. Two cases of these functions are considered: the Weibull and linear intensity function. The Bayesian approach with Gibbs sampling under the Markov Chain Monte Carlo (MCMC) algorithm is used to estimate the parameters of these functions. The most appropriate fitted model is selected by using Deviance Information Criteria (DIC) and use that appropriately fitted model to calculate the accumulated events of agricultural drought in a time interval of interest at each location. Ordinary Kriging (OK) is used to regionalize the parameters and present its spatial behavior. The results based on the DIC indicate that the Power Law Process (PLP) performs better than the linear intensity function, NHPP model. The interpolated parameter values for the appropriate model, their patterns, and fluctuations for the study area are efficiently presented using contour maps. It is a novel and straightforward approach to assess the selected model parameter values used to predict the accumulated drought events at un-sampled locations. The proposed framework might also help to analyze other spatial variables of interest and can be used for climate-change study, ecosystem modeling, etc. The findings can also help to make decisions for sustainable environmental management in Pakistan.



中文翻译:

使用非齐次泊松模型和适当模型参数区域化的农业干旱期分析

摘要

降水在农业中起主导作用,规律的降雨模式通常对农业至关重要;降雨过多或不足都可能有害。本文利用农业干旱指数来研究农业干旱时期,并对其在不同地点的强度进行分析。一些非齐次 Poisson 模型也用于计算感兴趣的时间间隔内农业干旱的概率(发生的次数)。假设农业干旱事件发生的次数是一个非齐次泊松过程 (NHPP),它有一个速率函数,它取决于一些必须估计的参数。考虑这些函数的两种情况:威布尔函数和线性强度函数。在马尔可夫链蒙特卡罗 (MCMC) 算法下使用吉布斯采样的贝叶斯方法来估计这些函数的参数。最合适的拟合模型是通过使用偏差信息标准 (DIC) 选择的,并使用该合适的拟合模型来计算每个位置感兴趣的时间间隔内农业干旱的累积事件。普通克里金法 (OK) 用于对参数进行区域化并呈现其空间行为。基于 DIC 的结果表明幂律过程 (PLP) 的性能优于线性强度函数、NHPP 模型。适当模型的内插参数值、它们的模式和研究区域的波动可以使用等高线图有效地呈现。这是一种评估所选模型参数值的新颖而直接的方法,用于预测未采样位置的累积干旱事件。拟议的框架还可能有助于分析其他感兴趣的空间变量,并可用于气候变化研究、生态系统建模等。研究结果还有助于为巴基斯坦的可持续环境管理做出决策。

更新日期:2021-07-29
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