Drought characterization using the Combined Terrestrial Evapotranspiration Index over the Indus, Ganga and Brahmaputra river basins

Dharpure, Jaydeo K.; Goswami, Ajanta; Patel, Akansha; Kulkarni, Anil V.; Thamban, Meloth

doi:10.1080/10106049.2020.1756462

Cited by 22 publications

(13 citation statements)

References 89 publications

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“…The simultaneous removal of more than one variable in the PWC is a possible analysis to be explored and some of the very recent formulations by Hu & Si (2021) offer flexibility to perform such analysis. Also, improved formulations like terrestrial combined terrestrial evapotranspiration index (CTEI) (Dharpure et al 2020;Elbeltagi et al 2021) involving both satellite data and hydrometeorological parameters can be used instead of ET 0 . In such cases, the more number of input variables and their roles can be considered which may give an improved understanding of the processes, which in turn may help better irrigation water management of the study area.…”

Section: Wavelet Analysis Using Partial Wavelet Coherencementioning

confidence: 99%

Multiscale coherence analysis of reference evapotranspiration of north-western Iran using wavelet transform

Sreedevi

Adarsh

Nourani

2021

Journal of Water and Climate Change

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This study applies different wavelet coherence formulations for investigating the multiscale associations of reference Evapotranspiration (ET0) of Tabriz and Urmia stations in North West Iran with five climatic variables, mean temperature (T), pressure (P), relative humidity (RH), wind speed (U) and Solar Radiation (SR). The relationships between different variables are quantified using the Average Wavelet Coherence (AWC) and the Percentage of Significant Coherence (PoSC). The Bivariate Wavelet Coherence (BWC) analysis showed that mean temperature (AWC = 0.73, PoSC = 59.18%) and wind speed (AWC = 0.63, PoSC = 49.55%) are the dominant predictors at Tabriz and Urmia stations. On considering the Multiple Wavelet Coherence (MWC) analysis, it is noticed that among the two-factor combinations, the T-P and P-RH combinations resulted in the highest coherence values for Tabriz and Urmia stations. T-U-SR combination produced the highest multiple wavelet coherence values among the three-factor cases for both the stations. The Partial Wavelet Coherence (PWC) analysis indicated a drastic reduction in coherence from the values of respective BWC analysis, indicating a strong interrelationship between different variables and ET0. The interrelationship between meteorological variables and ET0 is more apparent at Tabriz, while it is controlled more by the local-scale meteorology at Urmia.

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Section: Wavelet Analysis Using Partial Wavelet Coherencementioning

confidence: 99%

Multiscale coherence analysis of reference evapotranspiration of north-western Iran using wavelet transform

Sreedevi

Adarsh

Nourani

2021

Journal of Water and Climate Change

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“…This index was developed by Dharpure et al (2020) using hydrological and climatological conditions over the Indus, Ganga, and Brahmaputra river basins, and also highlighted that the CTEI was highly correlated with the ground observation wells. The CTEI index was derived using satellite data and hydro-meteorological parameters for the period 2003-2016.…”

Section: Ctei Description and Calculationmentioning

confidence: 99%

“…In this regard, several drought indices were introduced as a primary factor for predicting drought such as Palmer Drought Severity Index (PDSI) (Palmer 1965), Standardized Precipitation Index (SPI) (McKee et al 1993), and Standardized Runoff Index (SRI) (Shukla and Wood, 2008;Paul et al, 2018),Vegetation Drought Response Index (VegDRI) (Brown et al 2008), Standardized Stream flow Index (SSI) (Shukla and Wood 2008;Vicente-Serrano et al 2010),Standardized Precipitation Evapotranspiration Index (SPEI) (Vicente-Serrano et al 2010), Multivariate Standardized Drought Index (MSDI) (Hao and AghaKouchak 2013), Gravity Recovery and Climate Experiment (GRACE)derived Drought Severity Index (DSI) (Feng et al 2017;Zhao et al 2017),water storage deficit index (WSDI) (Sun et al 2018), Combined Climatologic Deviation Index (CCDI) (Sinha et al 2019), and Combined Terrestrial Evapotranspiration Index (CTEI) (Dharpure et al 2020).…”

Section: Introductionmentioning

confidence: 99%

“…Tirivarombo et al, (2018) proved that the SPEI can better describe drought than the SPI in the Kafue basin due to consider the evapotranspiration factor. Dharpure et al(2020) presented the CTEI by combining the GRACE and meteorological variables such as precipitation (P) and potential evapotranspiration (PET). They indicated that this index has several advantages compared with the other drought indices.…”

Section: Introductionmentioning

confidence: 99%

“…The main goal of the present study was to introduce a novel data-driven model for predicting extreme droughts over India based on the Combined Terrestrial Evapotranspiration Index (CTEI). This study employs the CTEI as the drought index because of its incorporation of the GRACE data and meteorological variables (i.e., P and PET), thus better show the drought than the other indices (Dharpure et al 2020). In this study, two novel models called SMO-SVM and greedy models are utilized for predicting the CTEI, which are not yet employed in developing drought prediction models.…”

Section: Introductionmentioning

confidence: 99%

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Applying GRACE satellite data and hydro-meteorological parameters for predicting the combined terrestrial evapotranspiration index (CTEI) based on an improved SVM and a greedy regression approach

Elbeltagi

Jamei²,

Srivastava

et al. 2021

Preprint

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Drought has become relatively one of the widespread extreme events that affect socio-economic sphere, ecological balance, environmental conditions, crop-yields, climate feedback mechanism, and water resources management. Monitoring of the drought is one of the challenging tasks, hence required to develop new techniques in prediction of this natural calamity accurately and timely. In this work, Combined Terrestrial Evapotranspiration Index (CTEI) that is forced through precipitation (P) and potential evapotranspiration (PET) is used for estimating drought. Also, this is a novel index developed for Indus-Ganga river basin, which also uses a Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage anomalies (TWSA) data. Thirteen input-variables comprised of GRACE-TWSA, Global Land Data Assimilation System GLDAS-TWSA, Groundwater Storage Anomaly (GWSA), P, Land Surface Temperature ( LST), Wind Speed (WS), Evapotranspiration (ET), Runoff, air temperature ( ), PET, net-shortwave (SWN) and long-wave (LWN) along with net radiation (NR) radiations were implemented for predicting CTEI. Further, thirteen different combinations were analyzed using an improved Sequential Minimal Optimization (SMO) algorithm for support vector machine (SVM) and a greedy linear regression method. Therefore, the objectives of this study are to develop several combinations based on machine learning models used for modeling CTEI, compare the accuracy and stability of these models in CTEI prediction, and select the best outcomes have been provided from these models. The performed error measures and statistical indicators prove that Combo 2 and 3 owing to SVM model are superior to the best combinations of Greedy model. Overall based on the results, the improved SVM is superior to Greedy model in estimating CTEI. SVM (Combo2: R=0.81) in spite of having a higher range of relative error, is superior to Greedy model (Combo3, R=0.77) for estimating CTEI. This method can be one of promising alternatives for estimating CTEI over major river basins across the world.

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Microwave and optical satellite data fusion for meteorological drought monitoring in the Ganga-Brahmaputra basin

Kaushik,

Pandey,

Dwivedi

2024

Theor Appl Climatol

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Drought characterization using the Combined Terrestrial Evapotranspiration Index over the Indus, Ganga and Brahmaputra river basins

Cited by 22 publications

References 89 publications

Multiscale coherence analysis of reference evapotranspiration of north-western Iran using wavelet transform

Multiscale coherence analysis of reference evapotranspiration of north-western Iran using wavelet transform

Applying GRACE satellite data and hydro-meteorological parameters for predicting the combined terrestrial evapotranspiration index (CTEI) based on an improved SVM and a greedy regression approach

Microwave and optical satellite data fusion for meteorological drought monitoring in the Ganga-Brahmaputra basin

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