Analysis of agricultural and hydrological drought periods by using non-homogeneous Poisson models: Linear intensity function

Ellahi, Asad; Almanjahie, Ibrahim M.; Hussain, Tajammal; Hashmi, Muhammad Zaffar; Faisal, Shahla; Hussain, Ijaz

doi:10.1016/j.jastp.2020.105190

Cited by 8 publications

(5 citation statements)

References 31 publications

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“…Historical detection of these variations is highly important for agriculture-based countries like Pakistan. In this context, several studies have been conducted in the region [24][25][26][27][28]. Ahmad et al [29] and Ahmed et al [30] reported a decreasing tendency in annual drought events over the northern, northeastern, and northwestern regions of Pakistan; however, in another study, Reggiani et al [31] reported an increasing tendency of these events for different subbasins of the upper Indus basin.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Analysis of Meteorological and Hydrological Droughts and Their Propagations

Abbas

Waseem

Ullah

et al. 2021

Water

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The quantitative description of relationships and propagation between different forms of drought at multiple spatiotemporal scales in various geographical locations is informative for early drought warning systems. This study intends to evaluate the historical hydrometeorological drought from 1984–2015 in the Soan River Basin, which is a critical water source for the Pothwar region of Pakistan. The reconnaissance drought index (RDI) and standardized runoff index (SRI) are used to characterize meteorological and hydrological droughts, respectively. The spatiotemporal variations of the RDI and SRI demonstrated that 2000 and 2010 were extremely dry and wet years, respectively. The results further reveal that the frequency of hydrometeorological drought events was higher in a shorter time scale (3 and 6 months), while durations featured longer timescales (9 and 12 months). The RDI and SRI time series showed a significant decreasing trend in terms of the Mann–Kendal and Sen slope estimator (SSE) results. Cross-correlation analysis for RDI and SRI with a time lag acknowledged the existence of a sequence between the RDI and SRI and a positive relationship between the two indices. The findings of this study could be helpful for better understanding drought variability and water resource management.

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Section: Introductionmentioning

confidence: 99%

Spatiotemporal Analysis of Meteorological and Hydrological Droughts and Their Propagations

Abbas

Waseem

Ullah

et al. 2021

Water

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“…Drought is a natural hazard, which is a result of a prolonged shortage of precipitation, high temperature and change in the weather pattern. Drought harms society, the economy and the natural environment, but it is difficult to identify and characterize (Asad Ellahi, Ibrahim, Almanjahie, Tajammal Hussain, Muhammad Zaffar Hashmi, Shahla Faisal, Jaz Hussain, 2020).…”

Section: Introductionmentioning

confidence: 99%

Application of Poisson Process to Drought Prediction – The Case Study of Yucheng City

Yang

Song²

2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Open-source R language can implement quantitative research using the flexibility, adaptability and simplicity of bayesian inference models. Counts of drought which are regarded as the realizations of drought “event” in a Poisson process follows an Gamma distribution (prior distribution) in the case study of Yucheng city with the support of R language. That is, the annual drought counts in Yucheng City during the 10 years from 1974 to 1983 is regarded as the 10 realizations of the Poisson process, and the drought counts in Yucheng city during the 10 years is described by the Poisson distribution. The Gamma (1,1) of theta is used as the prior distribution, and its posterior distribution is calculated to be Gamma (60,11). The sample value of theta is obtained by random sampling from the posterior distribution of theta, and a Poisson sample is randomly generated for each value as the predicted value. The posterior mean value of theta sampling results, the mean and quantiles of the predicted value, and the probability estimation of theta and the predicted value are calculated to obtain the highest density region of theta. Studies have shown that Poisson model of drought prediction in Yucheng city has more flexibility, adaptability and simplicity and can provide posterior mean, highest-posterior density of the data, and the drought prediction of Yucheng city is a feasible method on basis of the stochastic Poisson process, thereby providing better reference value for drought reduction.

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“…Some of these methodologies are the Precipitation Data Analysis (PDA) method, Water Balance (WB) method, Stream Flow Analysis (SFA) methods, and methods of using geographical and historical data. The frequently used drought analysis methods are based on the drought indices (Ellahi et al, 2020). Several drought indices have been recommended and proposed by various researchers to analyze drought events.…”

Section: Introductionmentioning

confidence: 99%

“…Here, we provide an efficient, effective, and novel framework for analyzing the drought periods to evaluate the aggregated number of drought events at unsampled locations. In the previous literature for the drought events or period analysis like Ellahi et al (2020) utilize only the NHPP based on a single intensity function only. But the comparative study of models improves the results significantly in applied statistics.…”

Section: Introductionmentioning

confidence: 99%

Agricultural drought periods analysis by using nonhomogeneous poisson models and regionalization of appropriate model parameters

Ellahi

Hussain

Hashmi

et al. 2021

Tellus A: Dynamic Meteorology and Oceanography

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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.

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Analysis of agricultural and hydrological drought periods by using non-homogeneous Poisson models: Linear intensity function

Cited by 8 publications

References 31 publications

Spatiotemporal Analysis of Meteorological and Hydrological Droughts and Their Propagations

Spatiotemporal Analysis of Meteorological and Hydrological Droughts and Their Propagations

Application of Poisson Process to Drought Prediction – The Case Study of Yucheng City

Agricultural drought periods analysis by using nonhomogeneous poisson models and regionalization of appropriate model parameters

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