Development of a new composite drought index for multivariate drought assessment

Waseem, Muhammad; Ajmal, Muhammad; Kim, Tae‐Woong

doi:10.1016/j.jhydrol.2015.04.044

Cited by 105 publications

(49 citation statements)

References 15 publications

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“…Introduced by Freidman (1991), MARS has previously been applied hydrology (Abraham and Steinberg, 2001;Sharda et al, 2008;Cheng and Cao, 2014;Deo et al, 2015;Kisi, 2015;Waseem et al, 2015) but its application for SPI forecasting is yet to be undertaken. MARS has an ability to analyze the contributions of each input where interactive effects from exploratory terms are utilized to model the predictand (Cheng and Cao, 2014).…”

Section: Multivariate Adaptive Regressions Splinementioning

confidence: 99%

Drought forecasting in eastern Australia using multivariate adaptive regression spline, least square support vector machine and M5Tree model

Deo

Kisi

Singh

2017

Atmospheric Research

259

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Drought forecasting using standardized metrics of rainfall is a core task in hydrology and water resources management. Standardized Precipitation Index (SPI) is a rainfall-based metric that caters for different time-scales at which the drought occurs, and due to its standardization, is well-suited for forecasting drought at different periods in climatically diverse region. This study advances drought modelling using multivariate adaptive regression splines (MARS), least square support vector machine (LSSVM), and M5Tree models by forecasting SPI in eastern Australia. MARS model incorporated rainfall as mandatory predictor with month (periodicity), SouthernOscillation Index, Pacific Decadal Oscillation Index and Indian Ocean Dipole, ENSO Modoki and Nino 3.0, 3.4 and 4.0 data added gradually. The performance was evaluated with root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (r 2 ). Best MARS model required different input combinations, where rainfall, sea surface temperature and periodicity were used for all stations, but ENSO Modoki and Pacific Decadal Oscillation indices were not required for Bathurst, Collarenebri and Yamba, and the Southern Oscillation Index was not required for Collarenebri. Inclusion of periodicity increased the r 2 value by 0.5-8.1% and reduced RMSE by 3.0-178.5 %. Comparisons showed that MARS superseded the performance of the other counterparts for three out of five stations with lower MAE by 15.0-73.9% and 7.3-42.2%, respectively. For the other stations, M5Tree was better than MARS/LSSVM with lower MAE by 13.8-13.4% and 25.7-52.2%, respectively, and for Bathurst, LSSVM yielded more accurate result. For droughts identified by SPI  -0.5, accurate forecasts were attained by A C C E P T E D M A N U S C R I P T ACCEPTED MANUSCRIPTMARS/M5Tree for Bathurst, Yamba and Peak Hill, whereas for Collarenebri and Barraba, M5Tree was better than LSSVM/MARS. Seasonal analysis revealed disparate results where MARS/M5Tree was better than LSSVM. The results highlight the importance of periodicity in drought forecasting and also ascertains that model accuracy scales with geographic/seasonal factors due to complexity of drought and its relationship with inputs and data attributes that can affect the evolution of drought events. KeywordsStandardized precipitation index, drought forecasting, multivariate adaptive regression spline, least square support vector machine, M5 Tree model IntroductionDrought is an insidious natural hazard that occurs as a normal, yet a recurrent feature in an arid, semi-arid, desert or rain-forested region (Wilhite et al., 2000a;Keyantash and Dracup, 2002;Vicente-Serrano, 2016). Drought impacts are exacerbated by shifts to warmer and drier conditions, leading to increased water demand compounded by population growth and consequent expansion of industrial, agricultural and energy sectors (McAlpine et al., 2009; IPCC, 2012). As a critical environmental issue, challenges posed by drought elicit increased alertness among hydrologists, a...

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Section: Multivariate Adaptive Regressions Splinementioning

confidence: 99%

Drought forecasting in eastern Australia using multivariate adaptive regression spline, least square support vector machine and M5Tree model

Deo

Kisi

Singh

2017

Atmospheric Research

259

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“…Earlier, indices were developed for characterization of spatial problems like composite hydrological drought index (Karamouz et al ; Waseem et al ) for areas where rainfall is not a primary source of water (Shafer and Dezman ); soil moisture deficit index and evapotranspiration deficit index for soil moisture deficit and ET deficit conditions (Narasimhan and Srinivasan ); multimetric index for river biological conditions (Emery et al ; Pont et al ); groundwater quality index (Saeedi et al ; Kumar and James ) for sea water intrusion in coastal aquifers (Tomaszkiewicz et al ), for lakes (Chow‐Fraser ) and such as developed a watershed sustainability index for a watershed (Chandniha et al ). Most of the earlier developed hydrologic index is based on single essential hydrologic components such as precipitation, water level, ET, sediment yield, and river flow etc.…”

Section: Introductionmentioning

confidence: 99%

“…Gajbhiye et al (2015aGajbhiye et al ( , 2015b developed the geomorphological erosion index using PCA for Shakkar Watershed, Madhya Pradesh, and Matthew (2012) developed a hydrological index by using HEC-ResSim model for stream flow in watershed and for ecologically acceptable water-level range in lake ecosystems by using water level fluctuations parameters (Liang et al 2014); evapotranspiration (ET) index for monitoring surface moisture status using Aqua/MODIS model (Nishida et al 2003); water resources vulnerability index using TOPSIS model (Jun et al 2011); and runoff based drought index was developed using statistical distribution (Wang et al 2009). Earlier, indices were developed for characterization of spatial problems like composite hydrological drought index (Karamouz et al 2009;Waseem et al 2015) for areas where rainfall is not a primary source of water (Shafer and Dezman 1982); soil moisture deficit index and evapotranspiration deficit index for soil moisture deficit and ET deficit conditions (Narasimhan and Srinivasan 2005); multimetric index for river biological conditions (Emery et al 2003;Pont et al 2007); groundwater quality index (Saeedi et al 2010; Kumar and James 2013) for sea water intrusion in coastal aquifers (Tomaszkiewicz et al 2014), for lakes (Chow-Fraser 2006) and such as developed a watershed sustainability index for a watershed (Chandniha et al 2014). Most of the earlier developed hydrologic index is based on single essential hydrologic components such as precipitation, water level, ET, sediment yield, and river flow etc.…”

Section: Introductionmentioning

confidence: 99%

Development of a Composite Hydrologic Index for Semi‐Arid Region of India

Jeet¹,

Singh

Sarangi

2019

Groundwater

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Rainfall is the major source for groundwater recharge in basins areas of central region of India. Now a day, the river basins are experiencing acute shortage of water which has resulted in lowering of groundwater level and drying up of water bodies. In order to maintain water sustainability; a composite hydrologic index was developed in the Betwa basin of Madhya Pradesh and Uttar Pradesh states, India. The index was developed using principal component analysis through hydrologic, topographic as well as geographic parameters derived from the Soil and Water Assessment Tool and MODFLOW model. The geomorphological parameters were categorized, on the basis of groundwater recharge potential and weight ranged from 1 to 4. The geomorphologic parameters, that is, soil type (T), slope (S), runoff ratio (R), and evapotranspiration (ET) were integrated into a single indicator of composite hydrologic index. Soil type and ET were the major factors that directly affected the groundwater recharge. These two parameters together explained 86% of total variability in the data. Based on the analysis of the four parameters that affected groundwater recharge, composite hydrologic index (CHI) was classified into very good, good, moderate, and low grade. The CHI was statistically validated using standardization methods. The index was developed as a water management tool to measure a sustainability state relative to a groundwater recharge potential, which allows for spatial and temporal comparison. This index will be helpful in natural resource management and will improve socioeconomic status of human population inhibiting in the semi‐arid region.

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“…During the last few decades, numerous drought severity indices have been suggested in the literature and have been extensively used in many geographical regions of the world (Mishra and Singh 2011;Zargar et al 2011;Tigkas et al 2012;Jiang et al 2015). However, we unfortunately cannot say that a certain index is absolutely better than the other indices (Kim et al 2014;Waseem et al 2015).…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the single variable-based assessment of drought may not be sufficient for significant and reliable drought assessment (Waseem et al 2015). Hence, the creation of a new multi-variate, multi-scalar, drought index that can incorporate and more effectively exploit readily available information regarding soil, water balance as well as climatic water balance with a time lag effect is needed for the proper assessment of drought (Zargar et al 2011).…”

Section: Introductionmentioning

confidence: 99%

Multivariate Drought Assessment Considering the Antecedent Drought Conditions

Waseem

Ajmal

Lee

et al. 2016

Water Resour Manage

Self Cite

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Several drought indices have been developed based on a single variable or multiple variables using very complex calculations. Antecedent conditions are quite significant for analyzing physical processes involved in the conceptual rainfall-runoff modeling and for proper assessment of drought. However, not much attention has been paid to these conditions in the development of drought indices. Hence, we developed an alternative index for drought assessment, i.e., the antecedent condition-based multivariate drought index (AMDI), by taking into consideration all of the forms of drought, including meteorological, agricultural, and hydrological drought, in combination with the antecedent drought conditions. By comparing the AMDI with the standardized precipitation index (SPI) and reconnaissance drought index (RDI), it was revealed that in most cases, the drought trend was more or less the same. However, some discrepancies were also observed. Moreover, by considering additional factors, i.e., the antecedent soil moisture conditions and balance, an approximately 6 % difference in the drought frequency was observed compared to that of the SPI and RDI results, leading to a significant and proper drought assessment. The AMDI was also identified as a multi-scalar, multivariate index, which aggregates the effects of multiple drought forms by maintaining the continuity during month-to-month transitions. Hence, we concluded that the AMDI could be considered as an alternative tool for significant drought assessment.

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Development of a new composite drought index for multivariate drought assessment

Cited by 105 publications

References 15 publications

Drought forecasting in eastern Australia using multivariate adaptive regression spline, least square support vector machine and M5Tree model

Drought forecasting in eastern Australia using multivariate adaptive regression spline, least square support vector machine and M5Tree model

Development of a Composite Hydrologic Index for Semi‐Arid Region of India

Multivariate Drought Assessment Considering the Antecedent Drought Conditions

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