Development of an integrated weighted drought index and its application for agricultural drought monitoring

Niaz, Muhammad Ahmad; Faiz, Muhammad Abrar; Yong-xia, Wei

doi:10.1007/s12517-021-06879-w

Cited by 8 publications

(5 citation statements)

References 47 publications

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“…Anomalies and percentiles (Champagne et al, 2011;Nicolai-Shaw et al, 2017;van Hateren et al, 2021;Vroege et al, Soil Moisture Deficiency Index (SMDI) (Pablos et al, 2017;Xu et al, 2018;Fang et al, 2021) Soil Water Deficit Index (SWDI) (Martínez-Fernández et al, 2016Mishra et al, 2017;Pablos et al, 2017;Paredes-Trejo and Barbosa, 2017;Bai et al, 2018;Fang et al, 2021;Paredes-Trejo et al, 2021;Zhou et al, 2021;Cao et al, 2022;Chatterjee et al, 2022;Wu et al, 2022)…”

Section: Indicator Equation Common Thresholdsmentioning

confidence: 99%

“…The simplest approaches are based on SM anomalies, percentiles or indices, such as the Standardized Brightness Temperature Index (SBTI), Standardized Soil Moisture Index (SSMI or SSI), Soil Moisture Deficit Index (SMDI), Soil Moisture Condition Index (SMCI) and Soil Moisture Anomaly Percentage Index (SMAPI) (Champagne et al, 2011;Scaini et al, 2015;Carrão et al, 2016;Velpuri et al, 2016;Cammalleri et al, 2017;Nicolai-Shaw et al, 2017;Pablos et al, 2017;Padhee et al, 2017;Eswar et al, 2018;Sadri et al, 2018;Xu et al, 2018;Blyverket et al, 2019;Ford and Quiring, 2019;Liu et al, 2019;Zhu et al, 2019;Modanesi et al, 2020;Fang et al, 2021;van Hateren et al, 2021;Vroege et al, 2021). There are many ways to prepare raw SM data to calculate robust anomalies from the long-term mean.…”

Section: Anomaly Approachesmentioning

confidence: 99%

“…Several other studies used a weighted sum of precipitation, temperature and SM, and some vegetation, where the weights are obtained with constrained optimization or Principal Component Analysis (Zhang and Jia, 2013;Hao et al, 2015;Zhang et al, 2016;Arun Kumar et al, 2021;Niaz et al, 2021). The resulting combined drought indicators have been evaluated against reference data such SPI or SPEI.…”

Section: Combined Drought Indicatorsmentioning

confidence: 99%

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Microwave remote sensing for agricultural drought monitoring: Recent developments and challenges

et al. 2022

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Agricultural droughts are extreme events which are often a result of interplays between multiple hydro-meteorological processes. Therefore, assessing drought occurrence, extent, duration and intensity is complex and requires the combined use of multiple variables, such as temperature, rainfall, soil moisture (SM) and vegetation state. The benefit of using information on SM and vegetation state is that they integrate information on precipitation, temperature and evapotranspiration, making them direct indicators of plant available water and vegetation productivity. Microwave remote sensing enables the retrieval of both SM and vegetation information, and satellite-based SM and vegetation products are available operationally and free of charge on a regional or global scale and daily basis. As a result, microwave remote sensing products play an increasingly important role in drought monitoring applications. Here, we provide an overview of recent developments in using microwave remote sensing for large-scale agricultural drought monitoring. We focus on the intricacy of monitoring the complex process of drought development using multiple variables. First, we give a brief introduction on fundamental concepts of microwave remote sensing together with an overview of recent research, development and applications of drought indicators derived from microwave-based satellite SM and vegetation observations. This is followed by a more detailed overview of the current research gaps and challenges in combining microwave-based SM and vegetation measurements with hydro-meteorological data sets. The potential of using microwave remote sensing for drought monitoring is demonstrated through a case study over Senegal using multiple satellite- and model-based data sets on rainfall, SM, vegetation and combinations thereof. The case study demonstrates the added-value of microwave-based SM and vegetation observations for drought monitoring applications. Finally, we provide an outlook on potential developments and opportunities.

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Section: Indicator Equation Common Thresholdsmentioning

confidence: 99%

Section: Anomaly Approachesmentioning

confidence: 99%

Section: Combined Drought Indicatorsmentioning

confidence: 99%

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Microwave remote sensing for agricultural drought monitoring: Recent developments and challenges

et al. 2022

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“…Using PDI, however, is much less accurate for yearly forecasts [58]. e present work is inspired by numerous peer-reviewed published articles, where single variable has been used for estimation and statistical analysis [26-28, 31-34, 54, 59] [61]. To sum up, proper and adequate integration of all relevant data and drought definitions with respect to the most common forms of monthly droughts in time and space, as well as climate change scenarios, should be addressed [61].…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

“…e present work is inspired by numerous peer-reviewed published articles, where single variable has been used for estimation and statistical analysis [26-28, 31-34, 54, 59] [61]. To sum up, proper and adequate integration of all relevant data and drought definitions with respect to the most common forms of monthly droughts in time and space, as well as climate change scenarios, should be addressed [61]. Since practically all these indices, including the study reported in this paper, are based on observed data as key performance indicators, drought indices may not produce universally agreed conclusions [58].…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

Computational Analysis for Rainfall Characterization and Drought Vulnerability in Peninsular India

Akhtar

Faroque

Roy

et al. 2021

Mathematical Problems in Engineering

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This paper analyzes the historical rainfall characterization and drought conditions in two major southern states of India, namely, Tamil Nadu and Karnataka, through estimation of meteorological drought indices, namely, drought index (DI), Palmer drought index (PDI), and standardized precipitation index (SPI). Monthly and yearly rainfall data, including temperature, have been considered for 110 years. Deficient rainfall conditions have been identified and compared using annual rainfall classification thresholds. Annual rainfall variability and trend have been estimated using Mann–Kendall test and Sen’s slope test. Comparative study on results implies that drought characterization using SPI may amply facilitate the standardization of threshold classification for severity and frequency. Based on threshold classification, it is found that Tamil Nadu witnessed on an average 11 years of moderate drought, 4.36 years of severe drought, and 1.32 years of extreme drought conditions with standard deviations of 4.28, 1.87, and 1.63 years, respectively, during the study period, whereas Karnataka witnessed on an average 9.74 years of moderate drought, 3.91 years of severe drought, and 2.30 years of extreme drought conditions with standard deviations of 4.54, 2.04, and 2.21 years, respectively, during the study period. According to the analysis, drought vulnerability in Tamil Nadu was higher than in Karnataka, based on the number of dry and wet years in terms of SPI threshold values and area covered over 110 years. Karnataka was more susceptible in terms of severity. When compared with other indices, analysis based on drought indices indicates that a single variable-based estimation using SPI is easy to assess and may be significant and definitive in terms of decision making for prioritizing drought mitigation measures in the study area in case of inadequate available data for multiple variable-based drought analysis.

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Remote sensing based Multivariate Hierarchical Agricultural Drought Index (MHADI) for India

Rani,

Sehgal,

Dhakar

et al. 2024

Theor Appl Climatol

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Development of an integrated weighted drought index and its application for agricultural drought monitoring

Cited by 8 publications

References 47 publications

Microwave remote sensing for agricultural drought monitoring: Recent developments and challenges

Microwave remote sensing for agricultural drought monitoring: Recent developments and challenges

Computational Analysis for Rainfall Characterization and Drought Vulnerability in Peninsular India

Remote sensing based Multivariate Hierarchical Agricultural Drought Index (MHADI) for India

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