A novel spatially weighted accumulative procedure for regional drought monitoring

Niaz, Rizwan; Hussain, Ijaz; Ali, Zulfiqar; Faisal, Muhammad; Elashkar, Elsayed Elsherbini; Shoukry, Alaa Mohamd; Gani, Showkat; Al-Deek, Fares Fawzi

doi:10.1080/16000870.2020.1838194

Cited by 17 publications

(42 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, the temporal behavior of the SPI-1 at various stations can be envisaged in Figure 5. Furthermore, the selected stations show more similar behavior in data over the region for a specific drought index [44]. However, varying distributions can be observed in selected stations ( Mathematical Problems in Engineering associated disciplines [51] and has more significant candidacy features for standardization.…”

Section: Resultsmentioning

confidence: 85%

“…Furthermore, the MBCCDSS uses the categorical values corresponding to each drought state. ese categorical values are specified for the various drought states that are classified according to Niaz et al [44]. Moreover, it assumes that each categorical sequence of the selected states instigates from one of the K components.…”

Section: Resultsmentioning

confidence: 99%

“…ese sequences reflect the steering behavior of drought states and reflect the importance of this on the application site. e drought states (extremely dry (ED), severely dry (SD), normal dry (ND), median dry (MD), median wet (MW), severely wet (SW), and extremely wet (EW)) are classified according to [44]. Now, let X i � (X i1 , X i2 , .…”

Section: Model-based Clustering Of Categorical Drought State Sequences (Mbccdss)mentioning

confidence: 99%

See 2 more Smart Citations

Prediction of Drought Severity Using Model-Based Clustering

Niaz

Hussain

Zhang

et al. 2021

Mathematical Problems in Engineering

Self Cite

View full text Add to dashboard Cite

Drought is a common climatic extreme that frequently spreads across large spatial and time scales. It affects living standard of people throughout the globe more than any other climate extreme. Therefore, the present study proposed a new technique, known as model-based clustering of categorical drought states sequences (MBCCDSS), for monthly prediction of drought severity to timely inform decision-makers to anticipate reliable actions and plans to minimize the negative impacts of drought. The potential of the proposed technique is based on the expectation-maximization (EM) algorithm for finite mixtures with first-order Markov model components. Moreover, the proposed approach is validated on six meteorological stations in the northern area of Pakistan. The study outcomes provide the basis to explore and frame more essential assessments to mitigate drought impacts for the selected stations.

show abstract

Section: Resultsmentioning

confidence: 85%

Section: Resultsmentioning

confidence: 99%

Section: Model-based Clustering Of Categorical Drought State Sequences (Mbccdss)mentioning

confidence: 99%

See 1 more Smart Citation

Prediction of Drought Severity Using Model-Based Clustering

Niaz

Hussain

Zhang

et al. 2021

Mathematical Problems in Engineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…Drought monitoring has the utmost consideration at the regional level as its immense impact on the countries' economy and stability (Zhai and Feng, 2009;Santos et al, 2019, Niaz et al, 2020. It emphasizes improved approaches that could be achieved before an event arises to decrease the negative effects of future droughts and enhance response efficiency (Wilhite et al, 2000;.…”

Section: Introductionmentioning

confidence: 99%

“…The regional recognition of drought can be perceived more comprehensively by using accumulative information of drought monitoring tools at various gauge stations. The different stations situated in a homogenous climatic area with inside relative qualities and transmissible in space emerge a few issues because of spatial and temporal information in data analysis preliminaries (Niaz et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

A new propagation-based framework to enhance competency in regional drought monitoring

Niaz

Zhang

Ali

et al. 2021

Tellus A: Dynamic Meteorology and Oceanography

Self Cite

View full text Add to dashboard Cite

Drought is considered a regional phenomenon that usually covers large territorial extensions. It can occur anywhere in the world with severe impacts on water resources and socioeconomic activities. Therefore, it is compulsory to develop reliable tools and execute national plans based on the preeminent information and characterization of drought. There are numerous drought monitoring tools available in the literature to handle spatial and temporal behavior of the drought for regional forecasting and early warning mitigation policies. Standardized Drought Indices (SDI) are frequently used for drought characterization and comparing climatic characteristics of the regions. However, analyzing the spatiotemporal dynamics of the region requires more reliable methods and procedures for drought monitoring. In this perspective, the present study proposes a novel procedure for monitoring drought at a regional level: The Regional Propagation Spatially Weighted Accumulated Drought Index (RPSWADI). The first phase of the proposed procedure is intended to accumulate information from various meteorological stations placed in the homogenous region. In the second phase, accumulated information is used to propagate a new drought index. The proposed procedure is validated on six homogenous meteorological stations of the Northern areas of Pakistan. Furthermore, the commonly used standardized drought indices are used to observe the performance of the proposed procedure. The choice of the indices depends on the climatic conditions of the specific region and will be quantified accordingly. Results show that the RPSWADI can incorporate the spatiotemporal structure of various time series in various stations.

show abstract

A novel self-adjusting weight approximation procedure to minimize non-identical seasonal effects in multimodel ensemble for accurate twenty-first century drought assessment

Naz,

Ali

2024

Stoch Environ Res Risk Assess

View full text Add to dashboard Cite

A novel spatially weighted accumulative procedure for regional drought monitoring

Cited by 17 publications

References 36 publications

Prediction of Drought Severity Using Model-Based Clustering

Prediction of Drought Severity Using Model-Based Clustering

A new propagation-based framework to enhance competency in regional drought monitoring

A novel self-adjusting weight approximation procedure to minimize non-identical seasonal effects in multimodel ensemble for accurate twenty-first century drought assessment

Contact Info

Product

Resources

About