Study on inter-seasonal and intra-seasonal relationships of meteorological and agricultural drought indices in the Rajasthan State of India

Dhakar, Rajkumar; Sehgal, Vinay Kumar; Pradhan, Sanatan

doi:10.1016/j.jaridenv.2013.06.001

Cited by 44 publications

(15 citation statements)

References 38 publications

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“…At present, research on the large-area response lag of remote sensing-derived variables, such as VIs have mainly been conducted on a monthly scale and have not explored in finer time scales. In addition, few previous studies have concentrated on the relation between vegetation anomaly and drought, since the vegetation index, rather than 'vegetation anomaly' index was used to analyze the relationship between vegetation and drought, it is difficult to remove the influence of non-drought factors (seasonal change and vegetation growth) on the changes of a vegetation index [44,49]. Some studies have used standard scores to characterize the anomalies of vegetation and rainfall, and other methods have been tried to calculate the time lag of vegetation's response to drought in more sophisticated way [50][51][52].…”

Section: Introductionmentioning

confidence: 99%

Mapping the Spatial-Temporal Dynamics of Vegetation Response Lag to Drought in a Semi-Arid Region

Wang

Sui

et al. 2019

Remote Sensing

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Drought, as an extreme climate event, affects the ecological environment for vegetation and agricultural production. Studies of the vegetative response to drought are paramount to providing scientific information for drought risk mitigation. In this paper, the spatial-temporal pattern of drought and the response lag of vegetation in Nebraska were analyzed from 2000 to 2015. Based on the long-term Daymet data set, the standard precipitation index (SPI) was computed to identify precipitation anomalies, and the Gaussian function was applied to obtain temperature anomalies. Vegetation anomaly was identified by dynamic time warping technique using a remote sensing Normalized Difference Vegetation Index (NDVI) time series. Finally, multilayer correlation analysis was applied to obtain the response lag of different vegetation types. The results show that Nebraska suffered severe drought events in 2002 and 2012. The response lag of vegetation to drought typically ranged from 30 to 45 days varying for different vegetation types and human activities (water use and management). Grasslands had the shortest response lag (~35 days), while forests had the longest lag period (~48 days). For specific crop types, the response lag of winter wheat varied among different regions of Nebraska (35-45 days), while soybeans, corn and alfalfa had similar response lag times of approximately 40 days.

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Section: Introductionmentioning

confidence: 99%

Mapping the Spatial-Temporal Dynamics of Vegetation Response Lag to Drought in a Semi-Arid Region

Wang

Sui

et al. 2019

Remote Sensing

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“…Zhou et al (2010) reported that drought events were detected in the early stage of crop growing season in north of China from the strong negative correlations. Dhakar et al (2013) shown the impact of drought on crop growth from the higher and lower correlation in the middle and the end of growing season, respectively, and drought can cause the shortening of a crop season.…”

Section: Correlation Analysis Between Vci and Spimentioning

confidence: 99%

“…In India, the effect of drought varied by locations and it can be observed from the correlation between NDVI and SPI at different timescale (Jain et al 2010). In addition, the relationship between SPI and the Trend Adjusted VCI (VCI Tadj ) was proved to be better at capturing the spatiotemporal pattern of drought for crop in un-irrigated and marginally irrigated areas (Dhakar et al 2013).…”

Section: Introductionmentioning

confidence: 99%

A combination of meteorological and satellite-based drought indices in a better drought assessment and forecasting in Northeast Thailand

2015

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Drought is a natural hazard which occurs in all climatic zones. The effect from drought can cause a serious problem for agricultural activities, economies and the environment. There is a need to characterize drought events in terms of drought severity, frequency and possibility of drought occurrence for better drought management. An examination of drought characteristics and drought severity using the Standardized Precipitation Index (SPI) and the Vegetation Condition Index (VCI) was carried out for different land cover types. The study examined how data mining techniques such as association rules could be used to elucidate the relationships between VCI and SPI in order to predict the possibility of drought occurrence. Rainfall datasets were collected from the Thai meteorological department for the period 1980–2009 and digitally encoded into a Geographic Information System database. SPI values were derived both temporally and spatially for quantitative measurement of drought events over the 30-year period. Monthly VCI values were calculated from NDVI data collected from year 2001 to 2009 using multi-temporal Terra MODIS Vegetation Indices Product (MOD13Q1). Data mining technique was introduced and applied to generate association rules between VCI and SPI to predict the possibility of drought occurrence. The results from multi-temporal SPI analysis shown drought event occurred more often for the 3- and 6-month SPI in October at the central and the northeastern part of the region. Spatial SPI revealed that high-drought-risk areas were in the southwest and extending to the central part of the region. The statistically significant correlations between monthly VCI and SPI at the multiple timescales were found for mixed deciduous forest in dry period. This result indicated vegetation condition for this forest type was sensitive for precipitation during dry period. Drought events were found to affect the rice crop in the central part of the region more, as observed from the negative correlation between VCI and SPI during growing season. The representative association rules from VCI and SPI revealed drought event also occurred for paddy field in the central part of the region. Drought periods within the growing season for this area are becoming more prevalent even with increase in annual rainfall. Shorter scale of SPI was found to be effective in characterizing drought conditions. This study combined the different level of software and dataset used which are able to predict future occurrence and severity of drought using the current condition. Results can be applied to assess drought severity and drought-affected areas for efficient drought management and planning

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“…Dhakar et al [12] studied the relationship between SPI (a meteorological drought indicator) and the satellite-derived vegetation condition index (VCI) (an indicator of agricultural droughts). They concluded that the relationship between meteorological and agricultural drought indicators improved with seasonal progression, indicating a time-varying relationship between the two variables.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Meteorological- and Agriculture-Related Drought Indicators across Ethiopia

Tsige

Uddameri

Forghanparast

et al. 2019

Water

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Meteorological drought indicators are commonly used for agricultural drought contingency planning in Ethiopia. Agricultural droughts arise due to soil moisture deficits. While these deficits may be caused by meteorological droughts, the timing and duration of agricultural droughts need not coincide with the onset of meteorological droughts due to soil moisture buffering. Similarly, agricultural droughts can persist, even after the cessation of meteorological droughts, due to delayed hydrologic processes. Understanding the relationship between meteorological and agricultural droughts is therefore crucial. An evaluation framework was developed to compare meteorologicaland agriculture-related drought indicators using a suite of exploratory and confirmatory tools. Receiver operator characteristics (ROC) was used to understand the covariation of meteorological and agricultural droughts. Comparisons were carried out between SPI-2, SPEI-2, and Palmer Z-index to assess intraseasonal droughts, and between SPI-6, SPEI-6, and PDSI for full-season evaluations. SPI was seen to correlate well with selected agriculture-related drought indicators, but did not explain all the variability noted in them. The correlation between meteorological and agricultural droughts exhibited spatial variability which varied across indicators. SPI is better suited to predict non-agricultural drought states than agricultural drought states. Differences between agricultural and meteorological droughts must be accounted for in order to devise better drought-preparedness planning.

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Study on inter-seasonal and intra-seasonal relationships of meteorological and agricultural drought indices in the Rajasthan State of India

Cited by 44 publications

References 38 publications

Mapping the Spatial-Temporal Dynamics of Vegetation Response Lag to Drought in a Semi-Arid Region

Mapping the Spatial-Temporal Dynamics of Vegetation Response Lag to Drought in a Semi-Arid Region

A combination of meteorological and satellite-based drought indices in a better drought assessment and forecasting in Northeast Thailand

Comparison of Meteorological- and Agriculture-Related Drought Indicators across Ethiopia

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