Fore-warning of early season agricultural drought condition over Indian region – a fractional wetness approach

2019 ◽  
Vol 35 (6) ◽  
pp. 569-588 ◽  
Author(s):  
Prabir Kumar Das ◽  
Dilip Kumar Das ◽  
Subrata Kumar Midya ◽  
Uday Raj ◽  
Vinay Kumar Dadhwal
Keyword(s):  
Indian Region ◽  
Agricultural Drought ◽  
Drought Condition ◽  
Early Season

Study on the correlation between meteorological and agricultural drought, based on remotely sensed indices

2020 ◽  
Author(s):  
Ruja Mansorian ◽  
Mohammad Zare ◽  
Guy Schumann
Keyword(s):  
Time Scales ◽  
Vegetation Index ◽  
Meteorological Drought ◽  
Lag Time ◽  
Irrigation Scheduling ◽  
Agricultural Drought ◽  
Standard Normal Distribution ◽  
Drought Indices ◽  
Landsat 8 ◽  
Drought Condition

<p>In this study, long-term time series of precipitation data were used for determining the drought condition using the standard precipitation index (SPI) for 3, 6 and 12 month time scales. The indicators were calculated with two methods: a) using a gamma distribution and transforming the probability of occurrence to standard normal distribution, b) using the non-parametric plotting position method. Then, the SPI values for two consequent years 2013-14 and 2014-15 were extracted from data to study on meteorological drought. The SPI index calculations showed that the first year had near normal, whereas the second year had extreme drought condition. In parallel, 34 Landsat 8 satellite images were downloaded during the indicated time period to determine normalized difference vegetation index (NDVI) and vegetation condition index (VCI) as agricultural drought indices. The mean values of VCI for each month were considered as representative value for drought condition of the area. When the agricultural and meteorological drought indices were determined, the correlation coefficient (r) were calculated for finding the relation between these types of droughts. the results show that the highest correlation between SPI-3,6 and 12-month time scales and VCI occurred in 4, 2 and 4 months lag time respectively, with corresponding r value of 0.67, 0.65 and 0.69. The best agreement between these indices with calculated lag time proves the hypothesis that agricultural drought occurs after meteorological drought. Therefore, the results could be applied by farmers to plan an appropriate irrigation scheduling for upcoming droughts, specially, in arid and semi-arid areas. It could be concluded that for having suitable planning in water scarcity condition, understanding the situation helps water planners have better insight about management polices to minimize the effects of this natural hazard on human. To sum up, finding a relation between different types of droughts is helpful for monitoring, predicting and detecting droughts to better prepare for drought phenomena and to minimize losses</p>

scholarly journals Mapping the Spatial and Temporal Variation of Agricultural and Metrological Drought Using Geospatial Techniques, Ethiopia

2020 ◽  
Author(s):  
Abebe Senamaw ◽  
Solomon Addisu ◽  
K.V. Suryabhagavan
Keyword(s):  
Temporal Variation ◽  
Crop Yield ◽  
Meteorological Drought ◽  
Condition Index ◽  
Agricultural Drought ◽  
Spatial And Temporal Variation ◽  
Drought Indices ◽  
Drought Risk ◽  
Drought Condition ◽  
Gis And Remote Sensing

Abstract Background Geographic Information System (GIS) and Remote Sensing play an important role for near real time monitoring of drought condition over large areas. The objective of this study was to assess spatial and temporal variation of agricultural and metrological drought using temporal image of eMODIS NDVI based vegetation condition index (VCI) and standard precipitation index (SPI). To validate the strength of drought indices correlation analysis was made between VCI and crop yield anomaly as well as SPI and crop yield anomaly. The results revealed that the year 2009 and 2015 were drought years while the 2001 and 2007 were wet years. There was also a good correlation between NDVI and rainfall (r=0.71), VCI and crop yield anomaly (0.72), SPI and crop yield anomaly (0.74). Frequency of metrological and agricultural drought was compiled by using historical drought intensity map. ResultThe result shows that there was complex and local scale variation in frequency of drought events in the study period. There was also no year without drought in many parts of the study area. Combined drought risk map also showed that 8%, 56%, 35% and 8% of study area were vulnerable to very severe, severe and moderate drought condition respectively. Conclusion In conclusion, the study area is highly vulnerable to agricultural and meteorological drought. Thus besides mapping drought vulnerable areas, integrating socioeconomic data for better understand other vulnerable factors were recommended.

Quantification of agricultural drought over Indian region: a multivariate phenology-based approach

2020 ◽  
Vol 101 (1) ◽  
pp. 255-274 ◽  
Author(s):  
Prabir Kumar Das ◽  
Rituparna Das ◽  
Dilip Kumar Das ◽  
Subrata Kumar Midya ◽  
Soumya Bandyopadhyay ◽  
...  
Keyword(s):  
Indian Region ◽  
Agricultural Drought

scholarly journals Mapping the spatial and temporal variation of agricultural and meteorological drought using geospatial techniques, Ethiopia

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Abebe Senamaw ◽  
Solomon Addisu ◽  
K. V. Suryabhagavan
Keyword(s):  
Temporal Variation ◽  
Crop Yield ◽  
Standardized Precipitation Index ◽  
Meteorological Drought ◽  
Precipitation Index ◽  
Agricultural Drought ◽  
Spatial And Temporal Variation ◽  
Drought Indices ◽  
Drought Risk ◽  
Drought Condition

Abstract Background Geographic Information System (GIS) and Remote Sensing play an important role for near real time monitoring of drought condition over large areas. The aim of this study was to assess spatial and temporal variation of agricultural and meteorological drought using temporal image of eMODIS NDVI based vegetation condition index (VCI) and standard precipitation index (SPI) from the year 2000 to 2016. To validate the strength of drought indices correlation analysis was made between VCI and crop yield anomaly as well as standardized precipitation index (SPI) and crop yield anomaly. Results The results revealed that the year 2009 and 2015 was drought years while the 2001 and 2007 were wet years. There was also a good correlation between NDVI and rainfall (r = 0.71), VCI and crop yield anomaly (0.72), SPI and crop yield anomaly (0.74). Frequency of metrological and agricultural drought was compiled by using historical drought intensity map. The result shows that there was complex and local scale variation in frequency of drought events in the study period. There was also no year without drought in many parts of the study area. Combined drought risk map also showed that 8%, 56% and 35% of the study area were vulnerable to very severe, severe and moderate drought condition respectively. Conclusions In conclusion, the study area is highly vulnerable to agricultural and meteorological drought. There was also no year without drought in many parts of the study area. Thus besides mapping drought vulnerable areas, integrating socio-economic data for better understand other vulnerable factors were recommended.

scholarly journals TRENDS AND VULNERABILITY ASSESSMENT OF METEOROLOGICAL AND AGRICULTURAL DROUGHT CONDITIONS OVER INDIAN REGION USING TIME-SERIES (1982–2015) SATELLITE DATA

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 453-459 ◽  
Author(s):  
R. Das ◽  
P. K. Das ◽  
S. Bandyopadhyay ◽  
U. Raj
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Empirical Distribution ◽  
Monsoon Season ◽  
Standardized Precipitation Index ◽  
Indian Region ◽  
Agricultural Drought ◽  
Drought Vulnerability ◽  
Drought Conditions

<p><strong>Abstract.</strong> The vulnerability and trends of meteorological as well as agricultural drought conditions over Indian region was studied using long-term (1982&amp;ndash;2015) gridded precipitation and time-series normalized difference vegetation index (NDVI) data. The Climate Hazards Group Infra-Red Precipitation with Station (CHIRPS) precipitation data (~5&amp;thinsp;km) was used to compute Standardized precipitation index (SPI) at 3-month time scale for Indian summer monsoon season (June-September). Subsequently, the long-term Global Inventory Modelling and Mapping Studies (GIMMS) time-series NDVI data (~8&amp;thinsp;km) was interpolated at daily scale and smoothened using Savitzky and Golay filtering method. Further, the time-series NDVI data was transformed into several phenological parameters using threshold and derivative approach. As integrated NDVI, i.e. the area under seasonal NDVI curve, is able to represent the anomalies in seasonal agricultural production, it was transformed into standardized vegetation index (SVI) using empirical distribution. Several drought parameters, e.g. magnitude and extent, were computed at district level based on the SPI and SVI values, where values with SPI or SVI less than minus one was considered as meteorological and agricultural drought year, respectively. The trends of drought magnitude and extent for both the meteorological and agricultural drought were estimated using Sen’s slope. The direction of trends and magnitude were found to be varying spatially across different parts of Indian region. Further, the mean SPI/SVI values along with drought frequency were utilized to categorize entire Indian agricultural area into different vulnerable zones during three decades separately. The overall drought vulnerability was found to be decreasing over time.</p>

scholarly journals Assessing early season drought condition using AMSR-E soil moisture product

2013 ◽  
Vol 4 (2) ◽  
pp. 164-186 ◽  
Author(s):  
Abhishek Chakraborty ◽  
M.V.R. Seshasai ◽  
C.S. Murthy ◽  
S.V.C. Kameswara Rao
Keyword(s):  
Soil Moisture ◽  
Drought Condition ◽  
Early Season

scholarly journals ASSESSMENT OF EARLY SEASON AGRICULTURAL DROUGHT USING REMOTE SENSING

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1691-1695
Author(s):  
B. R. Nikam ◽  
S. P. Aggarwal ◽  
P. K. Thakur ◽  
V. Garg ◽  
S. Roy ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Monsoon Season ◽  
Standardized Precipitation Index ◽  
Agricultural Drought ◽  
Agricultural System ◽  
Severe Drought ◽  
Study Region ◽  
Early Season ◽  
Prone Area ◽  
Drought Prone Area

Abstract. Drought is a stochastic natural hazard that is caused by intense and persistent shortage of precipitation. The initial shortage of rainfall subsequently impacts the agriculture and hydrology sectors. Marathwada region of India comes under highly drought prone area in the country. Recent times have shown the increase in occurrence of agricultural drought in the non-monsoon season. The deviation from normal rainfall in the month of October causes soil moisture deficit which triggers an agricultural drought in the early-Rabi season. The traditional remote sensing based agricultural drought monitoring indices lack in identifying the early-season (ES) drought. An attempt has been made in the present study, to map ES agricultural drought in the Aurangabad district of Marathwada region using remote sensing. The meteorological deficit in the month of October, has been assessed using Standardized Precipitation Index (SPI). Impact of meteorological fluctuations on agricultural system in terms of dryness/wetness was evaluated using the Shortwave Angel Slope Index (SASI) derived using MODIS (Terra) Level-3, 8 daily, surface reflectance data for the October months of 2001–2012. It was observed that the area experiences moderate to severe drought 5 times with 12 years of study period (2001–2012). SASI and its parameters were estimated for each week of October month. SASI maps were further classified in four categories viz. moist vegetation; dry vegetation; moist soil and dry soil. The detailed analyses if these maps indicate that agricultural stress occurs in this area even if there is no meteorological stress. However, whenever, there is meteorological stress the area under agricultural stress exceeds more than 50% of the study region. A frequency distribution map of ES drought was prepared to identify the most drought prone area of the district and to alternately identify the irrigated area of the district.

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