Drought monitoring and prediction using SPEI index and gene expression programming model in the west of Urmia Lake

Abbasi, Abbas; Khalili, Keivan; Behmanesh, Javad; Shirzad, Akbar

doi:10.1007/s00704-019-02825-9

Cited by 60 publications

(40 citation statements)

References 39 publications

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“…The dataset is composed of the remote sensing drought index and the output of an LDAS described below and of the cereal yield statistics on the agricultural provincial scale. In addition, cumulative rainfall amounts and the SPEI, which is considered to be a reference for drought monitoring in several studies [79][80][81][82], were extracted from ERA5 reanalysis surface variables.…”

Section: Datamentioning

confidence: 99%

Linkages between Rainfed Cereal Production and Agricultural Drought through Remote Sensing Indices and a Land Data Assimilation System: A Case Study in Morocco

et al. 2020

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In Morocco, cereal production shows high interannual variability due to uncertain rainfall and recurrent drought periods. Considering the socioeconomic importance of cereal for the country, there is a serious need to characterize the impact of drought on cereal yields. In this study, drought is assessed through (1) indices derived from remote sensing data (the vegetation condition index (VCI), temperature condition index (TCI), vegetation health ind ex (VHI), soil moisture condition index (SMCI) and soil water index for different soil layers (SWI)) and (2) key land surface variables (Land Area Index (LAI), soil moisture (SM) at different depths, soil evaporation and plant transpiration) from a Land Data Assimilation System (LDAS) over 2000–2017. A lagged correlation analysis was conducted to assess the relationships between the drought indices and cereal yield at monthly time scales. The VCI and LAI around the heading stage (March-April) are highly linked to yield for all provinces (R = 0.94 for the Khemisset province), while a high link for TCI occurs during the development stage in January-February (R = 0.83 for the Beni Mellal province). Interestingly, indices related to soil moisture in the superficial soil layer are correlated with yield earlier in the season around the emergence stage (December). The results demonstrate the clear added value of using an LDAS compared with using a remote sensing product alone, particularly concerning the soil moisture in the root-zone, considered a key variable for yield production, that is not directly observable from space. The time scale of integration is also discussed. By integrating the indices on the main phenological stages of wheat using a dynamic threshold approach instead of the monthly time scale, the correlation between indices and yield increased by up to 14%. In addition, the contributions of VCI and TCI to VHI were optimized by using yield anomalies as proxies for drought. This study opens perspectives for the development of drought early warning systems in Morocco and over North Africa, as well as for seasonal crop yield forecasting.

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Section: Datamentioning

confidence: 99%

Linkages between Rainfed Cereal Production and Agricultural Drought through Remote Sensing Indices and a Land Data Assimilation System: A Case Study in Morocco

et al. 2020

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“…Recent studies-e.g., [42,[56][57][58][59]-classified drought events with identical thresholds for SPEI and SPI. Therefore, we identified seven moderate to extreme events in the years 1982, 1986, 1987, 1994, 1997, 2005 and 2009.…”

Section: Drought Characteristics During the Period Of 1980-2017mentioning

confidence: 99%

Mechanisms for Severe Drought Occurrence in the Balsas River Basin (Mexico)

et al. 2021

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This work provides an assessment of the two most intense seasonal droughts that occurred over the Balsas River Basin (BRB) in the period 1980–2017. The detection of the drought events was performed using the 6 month scale standardized precipitation–evapotranspiration index (SPEI-6) and the 6 month standardized precipitation index (SPI-6) in October. Both indices were quite similar during the studied period, highlighting the larger contribution of precipitation deficits vs. temperature excess to the drought occurrence in the basin. The origin of the atmospheric water arriving to the BRB (1 May 1980–31 October 2017) was investigated by using a Lagrangian diagnosis method. The BRB receives moisture from the Caribbean Sea and the rest of the tropical Atlantic, the Gulf of Mexico, the eastern north Pacific and from three terrestrial evaporative sources: the region north of BRB, the south of BRB and the BRB itself. The terrestrial evaporative source of the BRB itself is by far the main moisture source. The two most intense drought events that occurred in the studied period were selected for further analysis. During the severe drought of 2005, the summertime sea surface temperature (SST) soared over the Caribbean Sea, extending eastward into a large swathe of tropical North Atlantic, which was accompanied by the record to date of hurricane activity. This heating generated a Rossby wave response with westward propagating anticyclonic/cyclonic gyres in the upper/lower troposphere. A cyclonic low-level circulation developed over the Gulf of Mexico and prevented the moisture from arriving to the BRB, with a consequent deficit in precipitation. Additionally, subsidence also prevented convection in most of the months of this drought period. During the extreme drought event of 1982, the Inter Tropical Convergence Zone (ITCZ) remained southern and stronger than the climatological mean over the eastern tropical Pacific, producing an intense regional Hadley circulation. The descent branch of this cell inhibited the development of convection over the BRB, although the moisture sources increased their contributions; however, these were bounded to the lower levels by a strong trade wind inversion.

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“…The results of proposed model were compared and validated against the nature-inspired algorithm and stochastic (time-series) model built by numerous drought indices (DIs). For instance, there are studies conducted on the SPI prediction using various versions of AI models [40,[51][52][53][54][55]. Memarian et al [56] applied the CANFIS model to predict the meteorological drought in Birjand, Iran using global climatic indicators and lagged values of SPI.…”

Section: Plos Onementioning

confidence: 99%

Drought index prediction using advanced fuzzy logic model: Regional case study over Kumaon in India

et al. 2020

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A new version of the fuzzy logic model, called the co-active neuro fuzzy inference system (CANFIS), is introduced for predicting standardized precipitation index (SPI). Multiple scales of drought information at six meteorological stations located in Uttarakhand State, India, are used. Different lead times of SPI were computed for prediction, including 1, 3, 6, 9, 12, and 24 months, with inputs abstracted by autocorrelation function (ACF) and partial-ACF (PACF) analysis at 5% significance level. The proposed CANFIS model was validated against two models: classical artificial intelligence model (e.g., multilayer perceptron neural network (MLPNN)) and regression model (e.g., multiple linear regression (MLR)). Several performance evaluation metrices (root mean square error, Nash-Sutcliffe efficiency, coefficient of correlation, and Willmott index), and graphical visualizations (scatter plot and Taylor diagram) were computed for the evaluation of model performance. Results indicated that the CANFIS model predicted the SPI better than the other models and prediction results were different for different meteorological stations. The proposed model can build a reliable expert intelligent system for predicting meteorological drought at multi-time scales and decision making for remedial schemes to cope with meteorological drought at the study stations and can help to maintain sustainable water resources management.

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Drought monitoring and prediction using SPEI index and gene expression programming model in the west of Urmia Lake

Cited by 60 publications

References 39 publications

Linkages between Rainfed Cereal Production and Agricultural Drought through Remote Sensing Indices and a Land Data Assimilation System: A Case Study in Morocco

Linkages between Rainfed Cereal Production and Agricultural Drought through Remote Sensing Indices and a Land Data Assimilation System: A Case Study in Morocco

Mechanisms for Severe Drought Occurrence in the Balsas River Basin (Mexico)

Drought index prediction using advanced fuzzy logic model: Regional case study over Kumaon in India

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