A Review on Drought Index Forecasting and Their Modelling Approaches

Tan, Yi Xun; Ng, Jing Lin; Huang, Yuk Feng

doi:10.1007/s11831-022-09828-2

Cited by 6 publications

(2 citation statements)

References 49 publications

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“…Alternative methods proposed in the literature to quantify droughts and drought propagation involve empirical [24], physics-based, conceptual, statistical [25], stochastic [26], probabilistic [27], or AI-based models such as artificial neural networks [28], support vector machines [29], decision trees [30], or hybrid models that combine these different models [31]. Empirical models are simple but are unable to capture nonlinear relationships among hydroclimatic variables.…”

Section: Introductionmentioning

confidence: 99%

Heuristic Data-inspired Scheme to Characterize Meteorological and Groundwater Droughts in Semi-arid Karstic Region under Warming Climate

Basagaoglu,

Sharma,

Chakraborty

et al. 2023

Preprint

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Study regionThe Edwards Aquifer Region is located in south-central Texas, United States.Study focusThe paper focuses on the development and implementation of a data-inspired heuristic drought identification scheme to (i) quantify the intensity, duration, and frequency of precipitation deficit- and high temperature-driven meteorological droughts (PMet- and TMet-droughts), and (ii) link their propagation to groundwater droughts (GW-droughts) using baseline hydroclimatic measures and prevailing drought conditions derived from historical climate data and regional mitigation strategies.New hydrological insights for the regionBased on the intensity, duration, and timing of PMet- and TMet-droughts in the semi-arid karstic region, we identified three distinct GW-droughts, including persistence-driven, preconditions-driven, and intensity-driven droughts. The analysis revealed that successive heavy precipitation events are needed to end GW-droughts in the region. The scheme also identified TMet-droughts with the longest dry spells, TMet- and PMet-droughts with the highest intensity, and GW-drought with the second-highest intensity on record all occurred over the past 15 years. These findings provide evidence for a warming climate, intensified meteorological droughts, and increasing stress on the aquifer. Among the artificial intelligence models used, Extremely Randomized Trees (ERT) predicted time series of intensity & duration of GWdroughts from hydroclimatic features with high accuracy. Moreover, the ERT classifier revealed that the duration of PMet droughts and the intensity of TMet droughts are the topmost decisive features in predicting GW-drought intensity in the region.

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Section: Introductionmentioning

confidence: 99%

Heuristic Data-inspired Scheme to Characterize Meteorological and Groundwater Droughts in Semi-arid Karstic Region under Warming Climate

Basagaoglu,

Sharma,

Chakraborty

et al. 2023

Preprint

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“…Consequently, numerous global, regional, and national studies have been conducted to establish evidence of climate change/variability and its implications [3][4][5][6]. Similarly, many efforts have been made to monitor and predict extreme climate events such as droughts [7,8]. As Dunn et al [9] mentioned, climate change/variability is expected to lead to the intensification of the global hydrological cycle, exerting direct influences on overall water resource availability for human and agricultural consumption.…”

Section: Introductionmentioning

confidence: 99%

Hydroclimatic Trends and Drought Risk Assessment in the Ceyhan River Basin: Insights from SPI and STI Indices

et al. 2023

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This study examined the spatiotemporal climate variability over the Ceyhan River basin in Southern Anatolia, Türkiye using historical rainfall and temperature observations recorded at 15 meteorology stations. Various statistical and geostatistical techniques were employed to determine the significance of trends for each climatic variable in the whole basin and its three sub-regions (northern, central, and southern regions). The results revealed that the recent years in the basin were generally warmer compared with previous years, with a temperature increase of approximately 4 °C. The standardized temperature index analysis indicated a shift towards hotter periods after 2005, while the coldest periods were observed in the early 1990s. The spatial distribution of temperature showed non-uniform patterns throughout the basin. The first decade of the study period (1975–1984) was characterized by relatively cold temperatures, followed by a transition period from cold to hot between 1985 and 2004, and a hotter period in the last decade (2005–2014). The rainfall analysis indicated a decreasing trend in annual rainfall, particularly in the northern and central regions of the basin. However, the southern region showed an increasing trend in annual rainfall during the study period. The spatial distribution of rainfall exhibited considerable variability across the basin, with different regions experiencing distinct patterns. The standardized precipitation index analysis revealed the occurrence of multiple drought events throughout the study period. The most severe and prolonged droughts were observed in the years 1992–1996 and 2007–2010. These drought events had significant impacts on water availability and agricultural productivity in the basin.

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Improving multi-month hydrological drought forecasting in a tropical region using hybridized extreme learning machine model with Beluga Whale Optimization algorithm

Hameed,

Mohd Razali,

Wan Mohtar

et al. 2023

Stoch Environ Res Risk Assess

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A Review on Drought Index Forecasting and Their Modelling Approaches

Cited by 6 publications

References 49 publications

Heuristic Data-inspired Scheme to Characterize Meteorological and Groundwater Droughts in Semi-arid Karstic Region under Warming Climate

Heuristic Data-inspired Scheme to Characterize Meteorological and Groundwater Droughts in Semi-arid Karstic Region under Warming Climate

Hydroclimatic Trends and Drought Risk Assessment in the Ceyhan River Basin: Insights from SPI and STI Indices

Improving multi-month hydrological drought forecasting in a tropical region using hybridized extreme learning machine model with Beluga Whale Optimization algorithm

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