Composite Agrometeorological Drought Index Accounting for Seasonality and Autocorrelation

Bateni, M. Mehdi; Behmanesh, Javad; Michele, Carlo De; Bazrafshan, Javad; Rezaie, Hossein

doi:10.1061/(asce)he.1943-5584.0001654

Cited by 18 publications

(9 citation statements)

References 44 publications

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“…They calculated both of the indices for 1-12 month time window (which monitors meteorological, hydrological and agricultural droughts simultaneously) and showed that MSPI outperforms JDI for illustrating the intensity and weakness of drought in Iran's climates. Bateni et al [10] used MSPI for introducing the agrometeorological drought indices (AMDI-SA). In addition, MSPI prediction has been reported for Iranian climates in Aghelpour et al [11] study for agricultural drought forecasting.…”

Section: Introductionmentioning

confidence: 99%

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A Theoretical Approach for Forecasting Different Types of Drought Simultaneously, Using Entropy Theory and Machine-Learning Methods

Aghelpour

Mohammadi

Biazar

et al. 2020

IJGI

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Precipitation deficit can affect different natural resources such as water, soil, rivers and plants, and cause meteorological, hydrological and agricultural droughts. Multivariate drought indexes can theoretically show the severity and weakness of various drought types simultaneously. This study introduces an approach for forecasting joint deficit index (JDI) and multivariate standardized precipitation index (MSPI) by using machine–learning methods and entropy theory. JDI and MSPI were calculated for the 1–12 months’ time window (JDI1–12 and MSPI1–12), using monthly precipitation data. The methods implemented for forecasting are group method of data handling (GMDH), generalized regression neural network (GRNN), least squared support vector machine (LSSVM), adaptive neuro-fuzzy inference system (ANFIS) and ANFIS optimized with three heuristic optimization algorithms, differential evolution (DE), genetic algorithm (GA) and particle swarm optimization (PSO) as meta-innovative methods (ANFIS-DE, ANFIS-GA and ANFIS-PSO). Monthly precipitation, monthly temperature and previous amounts of the index’s values were used as inputs to the models. Data from 10 synoptic stations situated in the widest climatic zone of Iran (extra arid-cold climate) were employed. Optimal model inputs were selected by gamma test and entropy theory. The evaluation results, which were given using mean absolute error (MAE), root mean squared error (RMSE) and Willmott index (WI), show that the machine learning and meta-innovative models can present acceptable forecasts of general drought’s conditions. The algorithms DE, GA and PSO, could improve the ANFIS’s performance by 39.4%, 38.7% and 22.6%, respectively. Among all the applied models, the GMDH shows the best forecasting accuracy with MAE = 0.280, RMSE = 0.374 and WI = 0.955. In addition, the models could forecast MSPI better than JDI in the majority of cases (stations). Among the two methods used to select the optimal inputs, it is difficult to select one as a better input selector, but according to the results, more attention can be paid to entropy theory in drought studies.

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Section: Introductionmentioning

confidence: 99%

“…The variation of forecasts and observations for the MSPI prediction in all of the stations; The numbers of the subplots refer to the stations including Anar (1), Biarjmand (2), Boshrouyeh (3), East Isfahan (4), Isfahan (5), Kabootarabad (6), Kashan (7), Marvast (8), Naein(9) and Yazd(10).…”

mentioning

confidence: 99%

A Theoretical Approach for Forecasting Different Types of Drought Simultaneously, Using Entropy Theory and Machine-Learning Methods

Aghelpour

Mohammadi

Biazar

et al. 2020

IJGI

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“…For example the multivariate standardized drought index (MSDI; Hao and Aghakouchak, 2013), which combines the SPI and the standardized soil moisture index (SSI), uses copulas to form joint probabilities of precipitation and soil moisture content, while the joint drought index (JDI; Kao and Govindaraju, 2010) does the same for obtaining the joint probabilities while considering precipitation and streamflow. The composite agrometeorological drought index accounting for seasonality and autocorrelation (AMDI-SA) combines two drought indices, the modified SPI and the modified SSI, employing both the copula concept and the Kendall function (Bateni et al, 2018). The use of copulas seems promising and is highly effective when dealing with two or more variables.…”

Section: Introductionmentioning

confidence: 99%

A joint probabilistic index for objective drought identification: the case study of Haiti

Monteleone

Bonaccorso

Martina

2020

Nat. Hazards Earth Syst. Sci.

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Abstract. Since drought is a multifaceted phenomenon, more than one variable should be considered for a proper understanding of such an extreme event in order to implement adequate risk mitigation strategies such as weather or agricultural indices insurance programmes or disaster risk financing tools. This paper proposes a new composite drought index that accounts for both meteorological and agricultural drought conditions by combining in a probabilistic framework two consolidated drought indices: the standardized precipitation index (SPI) and the vegetation health index (VHI). The new index, called the probabilistic precipitation vegetation index (PPVI), is scalable, transferable all over the globe and can be updated in near real time. Furthermore, it is a remote-sensing product, since precipitation is retrieved from satellite data and the VHI is a remote-sensing index. In addition, a set of rules to objectively identify drought events is developed and implemented. Both the index and the set of rules have been applied to Haiti. The performance of the PPVI has been evaluated by means of a receiver operating characteristic curve and compared to that of the SPI and VHI considered separately. The new index outperformed SPI and VHI both in drought identification and characterization, thus revealing potential for an effective implementation within drought early-warning systems.

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“…For example the Multivariate Standardized Drought Index (MSDI) (Hao and Aghakouchak, 2013), which combines the SPI and the Standardized Soil Moisture Index (SSI), uses copula to form joint probabilities of precipitation and soil moisture content, while the Joint Drought Index (JDI) (Kao and Govindaraju, 2010) does the same for obtaining the joint probabilities while considering precipitation and streamflow. AMDI-SA combines two drought indices, the Modified SPI, and the Modified SSI, employing both the copula concept and the Kendall function (Bateni et al, 2018). The use of copulas seems promising and is highly effective when dealing with two or more variables.…”

Section: Introductionmentioning

confidence: 99%

“…CC BY 4.0 License. (Bateni et al, 2018) 31 https://doi.org/10.5194/nhess-2019-296 Preprint. Discussion started: 10 September 2019 c Author(s) 2019.…”

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confidence: 99%

A joint probabilistic index for objective drought identification: the case study of Haiti

Monteleone¹,

Bonaccorso²,

Martina³

2019

Preprint

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Abstract. Since drought is a multifaceted phenomenon, more than one variable should be considered for a proper understanding of such extreme event in order to implement adequate risk mitigation strategies such as weather or agricultural indices insurance programs, or disaster risk financing tools. This paper proposes a new composite drought index that accounts for both meteorological and agricultural drought conditions, by combining in a probabilistic framework two consolidated drought indices: the Standardized Precipitation Index (SPI) and the Vegetation Health Index (VHI). The new index, called Probabilistic Precipitation Vegetation Index (PPVI), is scalable, transferable all over the globe and can be updated in near-real time. Furthermore, it is a remote-sensing product, since precipitation are retrieved from satellite and the VHI is a remote-sensing index. In addition, a set of rules to objectively identify drought events is developed and implemented. Both the index and the set of rules have been applied to Haiti. The performance of PPVI has been evaluated by means of the Receiver Operating Characteristics curve and compared to the ones of SPI and VHI considered separately. The new index outperformed SPI and VHI both in drought identification and characterization, thus revealing potential for an effective implementation within drought early warning systems.

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Composite Agrometeorological Drought Index Accounting for Seasonality and Autocorrelation

Cited by 18 publications

References 44 publications

A Theoretical Approach for Forecasting Different Types of Drought Simultaneously, Using Entropy Theory and Machine-Learning Methods

A Theoretical Approach for Forecasting Different Types of Drought Simultaneously, Using Entropy Theory and Machine-Learning Methods

A joint probabilistic index for objective drought identification: the case study of Haiti

A joint probabilistic index for objective drought identification: the case study of Haiti

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