Investigating the propagation of droughts under the influence of large-scale climate indices in India

Das, Subhadarsini; Das, Jew; Umamahesh, N. V.

doi:10.1016/j.jhydrol.2022.127900

Cited by 32 publications

(5 citation statements)

References 105 publications

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“…The current analysis focuses just on one human-caused disturbance (reservoir) and one natural disturbance (snow). However, additional modifications such as changes in land use land cover (Omer et al 2020) and large-scale climatic factors like ENSO (Das et al 2022) can be examined more extensively in future studies.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of the impact of anthropogenic storage on the hydrological drought propagation in two contrasting semi-arid river basins

Pachore,

Agrawal,

Omonov

et al. 2024

Journal of Water and Climate Change

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This study quantitatively assessed the impacts of reservoirs on drought propagation in two semi-arid river basins: the Tapi basin with the Ukai reservoir in India (South Asia) and the Chirchik basin with the Charvak reservoir in Uzbekistan (Central Asia). Meteorological and hydrological droughts were characterized and analyzed using the Standardized Precipitation Index (SPI) and the Standardized Streamflow Index (SSI). Both river basins, especially in upstream reservoir areas, exhibited a notable correlation between hydrological drought (HD) and meteorological drought (MD). Reservoir operations resulted in lower MD–HD correlation in downstream areas for shorter SPI timescales, attributed to the influence of reservoir regulation. Hit score-based evaluations indicated reservoir operation-induced changes in drought propagation for both river basins. Due to the contrasting characteristics, the river basins showed a significant variation in the monthly lag time values, with distinct influences from monsoon (Tapi) and snow melting (Chirchik). In the Chirchik basin, the snowmelt season (April–September) had a shorter propagation time from MD to HD (average DPT: 5 months) and a longer time in the accumulation period (October–March; average DPT: 6 months). In the Tapi basin, influenced by the monsoon, the propagation time was shorter (June–September; average DPT: 4 months) and longer in the pre-monsoon period (average DPT: 6 months).

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Section: Discussionmentioning

confidence: 99%

Evaluation of the impact of anthropogenic storage on the hydrological drought propagation in two contrasting semi-arid river basins

Pachore,

Agrawal,

Omonov

et al. 2024

Journal of Water and Climate Change

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“…Naturally, the performance of a single weak learner is expected to be inadequate. Hence, the objective of AdaBoost is to iteratively generate a series of weak learners denoted as G(X i ), which are subsequently combined to construct a robust learner denoted as H(X i ), utilizing a specific combination strategy outlined in Equation (6). The combination strategy incorporates the weight of the weak learner (α k ) and G(X i ) as…”

Section: Adaptive Boosting (Adaboost) Regressionmentioning

confidence: 99%

“…This inherent variability necessitates an intricate and region-based understanding of droughts to deal with their impacts. While the increase in the frequency and severity of extreme weather phenomena can be linked to global warming [4][5][6][7][8], shorterterm events like meteorological and agricultural droughts often exhibit more complex behavior. Thus, it is vital to accurately forecast droughts and establish drought early warning systems for effective planning and resilience [9].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Meteorological Drought Modeling Accuracy Using Hybrid Boost Regression Models: A Case Study from the Aegean Region, Türkiye

et al. 2023

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The impact of climate change has led to significant changes in hydroclimatic patterns and continuous stress on water resources through frequent wet and dry spells. Hence, understanding and effectively addressing the escalating impact of climate change on hydroclimatic patterns, especially in the context of meteorological drought, necessitates precise modeling of these phenomena. This study focuses on assessing the accuracy of drought modeling using the well-established Standard Precipitation Index (SPI) in the Aegean region of Türkiye. The study utilizes monthly precipitation data from six stations in Cesme, Kusadasi, Manisa, Seferihisar, Selcuk and Izmir at Kucuk Menderes Basin covering the period from 1973 to 2020. The dataset is divided into three sets, training (60%), validation (20%), and testing (20%) sets. The study aims to determine the SPI-3, SPI-6 and SPI-12 using a multi-station prediction technique. Three boosting regression models (BRMs), namely Extreme Gradient Boosting (XgBoost), Adaptive Boosting (AdaBoost), and Gradient Boosting (GradBoost), were employed and optimized with the help of the Weighted Mean of Vectors (INFO) technique. Model performances were then evaluated with the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Coefficient of Determination (R2) and the Willmott Index (WI). Results demonstrated a distinct superiority of the XgBoost model over AdaBoost and GradBoost in terms of accuracy. During the test phase, the XgBoost model achieved RMSEs of 0.496, 0.429 and 0.389 for SPI-3, SPI-6 and SPI-12, respectively. The WIs were 0.899, 0.901 and 0.825 for SPI-3, SPI-6 and SPI-12, respectively. These are considerably lower than the corresponding values obtained by the other models. Yet, the comparative statistical analysis further underscores the effectiveness of XgBoost in modeling extended periods of drought in the Aegean region of Türkiye.

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“…In recent years, global drought events are frequent and researchers have conducted several national‐scale studies on drought propagation, such as those in the United States (Apurv & Cai, 2020; Tijdeman et al., 2018), German (Stahl & Heudorfer, 2017), the United Kingdom (Barker et al., 2016), South Korea (Bae et al., 2019), India (Bhardwaj et al., 2020; Das et al., 2022), Brazil (Bevacqua et al., 2021), and Thailand (Zhao et al., 2022). Due to the influence of the monsoon climate, China is also a country prone to drought (Long et al., 2020; Xu et al., 2022; X. Yang et al., 2020).…”

Section: Introductionmentioning

confidence: 99%

The Impact of Climatic Conditions, Human Activities, and Catchment Characteristics on the Propagation From Meteorological to Agricultural and Hydrological Droughts in China

Cheng,

Xu,

Chen

et al. 2023

JGR Atmospheres

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Droughts are one of the most frequent and destructive natural disasters worldwide. In the past decades, drought events in China are frequent and caused severe socio‐economic losses. To better predict and manage droughts, the spatiotemporal characteristics of the three types of droughts and propagation time (PT) from meteorological to agricultural and hydrological droughts in China during 1982–2014 were analyzed based on drought indices, while the causes of drought propagation were discussed. The results showed that meteorological droughts exhibited an insignificant trend. Agricultural droughts mainly aggravated in the northeastern and central regions. And the hydrological droughts were long‐lasting and exacerbated in most areas. The propagation speed from meteorological to agricultural and hydrological droughts was extremely rapid (1–2 months) in southeast China, and the relationships among droughts were close (correlation coefficient/R > 0.6). The propagation from meteorological to hydrological droughts was slower (6–8 months) in central China. In northwest China, the association between meteorological and hydrological droughts was weak (R < 0.4). Climatic conditions (especially temperature) played a dominant role in the propagation from meteorological to agricultural and hydrological droughts, explaining 63.3% and 52.6% of the variations in the PT, respectively. Urbanization, agricultural activities, elevation, and vegetation contributed to the propagation from meteorological to agricultural droughts. Reservoirs, agricultural activities, and vegetation also affected the propagation from meteorological to hydrological droughts by regulating hydrological processes. These findings are of vital significance to the prediction, warning, and management of different droughts.

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Investigating the propagation of droughts under the influence of large-scale climate indices in India

Cited by 32 publications

References 105 publications

Evaluation of the impact of anthropogenic storage on the hydrological drought propagation in two contrasting semi-arid river basins

Evaluation of the impact of anthropogenic storage on the hydrological drought propagation in two contrasting semi-arid river basins

Enhancing Meteorological Drought Modeling Accuracy Using Hybrid Boost Regression Models: A Case Study from the Aegean Region, Türkiye

The Impact of Climatic Conditions, Human Activities, and Catchment Characteristics on the Propagation From Meteorological to Agricultural and Hydrological Droughts in China

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