Effectiveness of Drought Indicators in Characterizing Past Droughts in the North-Western Part of Bangladesh (original) (raw)
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Natural Hazards, 2015
Several drought indices have been developed during the past decades for monitoring the onset, duration and intensity of drought in different agro-climatic regions. The present study attempts to monitor drought in two underprivileged districts, i.e., Mewat of Haryana and Dhar of Madhya Pradesh state of India, using the remote-sensing-derived Vegetation Condition Index (VCI), meteorological-based Standardized Precipitation Index (SPI) and hydrological-based Standardized Water Level Index (SWI). The time series SPOT VGT NDVI data of the rain-fed crop season (kharif) were used for a 13-year period (1998-2010) to assess the long-term vegetation conditions and compare with the meteorological and hydrological based drought indices. It was observed that the NDVI profile of the crop-growing season was remarkably shifted and shortened during drought years, indicating a delay in crop sowing. A detailed spatiotemporal analysis of drought dynamics was carried out using the VCI, which revealed the occurrence of a severe drought in Mewat and Dhar during the year 2002 and 2008, respectively. The correlation coefficient obtained between the VCI and SPI in Dhar (r = 0.55) and Mewat (r = 0.74) shows good agreement between satellite-derived and meteorological drought indices. However, it is also noteworthy that the correlation coefficient between the VCI and SPI is mainly region specific and varies with timescale. In spite of good agreement between these two indices during severe drought years, the drought estimates were found non-analogous during the years with moderate drought. The study also shows that hydrological drought may not
remote sensing based agricultural drought assessment journal.pdf
Increasing temperature and altered precipitation patterns, leads to the extreme weather events like Drought which drastically affects the agricultural production. Agricultural drought is nothing but the decline in the productivity of crops due to irregularities in the rainfall as well as decrease in the soil moisture, which in turn affects the economy of the nation. As the Indian agriculture is largely dependent on the Monsoon, a slight change in it affects the production as well as the crop yield drastically. The agricultural drought monitoring, assessment as well as management can be done more accurately with the help of geospatial techniques like Remote Sensing. Krishnagiri is an important district in the part of Tamilnadu. The study area falls between North latitudes 12° 16' N & 12° 88' N and East longitude 77° 50' E & 78° 55' E (Fig. 1) and covers an area about 5143 km2It is a drought prone region and falls within the most arid band of the country. The district relies on the traditional agricultural based economy; hence the impact of drought on the agriculture not only affects the production but also the livelihood of common man. The purpose of the study is to analyze the vegetation stress in the region krishnagiri district with the calculation of NDVI values and the land surface change classification). The MODIS data is used for the calculation of NDVI as well as Land surface temperature. The Combination of (NDVI) normalized difference vegetation index and LST, provides very useful information for agricultural drought monitoring and early warning system for the farmers. By calculating the correlation between rainfall analysis and NDVI, it can be clearly noticed that they show a high negative correlation. The correlation between Rainfall analysis and NDVI is -0.635 for the -0.586 for the year 2017.The LST when correlated with the vegetation index it can be used to detect the agricultural drought of a region, as demonstrated in this work.
Assessment of drought conditions using HJ-1A/1B data: a case study of Potohar region, Pakistan
Geomatics, Natural Hazards and Risk
Drought is a natural disaster which causes global damages and affects people. In this work, a comparative study of different drought indices such as Normalized Difference Vegetation Index (NDVI), Deviation NDVI (DevNDVI), Vegetation Condition Index (VCI) and Temperature Condition Index (TCI) from HJ-1A/1B multispectral data is discussed. These indices have shown potential to detect the drought severity. The objective of this study is to monitor drought in the Potohar region using HJ-1A/1B satellite data, from late November to April during 2009-2014. Additionally, results obtained from satellite data have been verified using ground-based rainfall and crop yield data. The results concluded that the Potohar region faced drought condition in 2010, which is further verified by ground data. Furthermore, NDVI and VCI in this region are found more effective than other drought indices. On the basis of validation of individual drought index with crop yield data, each index is assigned weight accordingly. Moreover, the combination of indices has the ability to detect time periods where drought is affecting the yield production. Regular monitoring and mapping of satellite-based drought indices would play an important role in predicting drought conditions.
Drought Monitoring for Northern Part of Iraq Using Temporal NDVI and Rainfall Indices
Environmental Remote Sensing and GIS in Iraq
Climate change is the major global challenge facing water resources managers. Drought is a natural hazard temporarily affecting almost every region in the world. In this study, the climate change in term of rainfall fluctuation in the northern part of Iraq (Mosul, Kirkuk and Salah Al-Din) has been investigated using a set of data containing monthly precipitation for the period from 1980 to 2010, and the MODIS time series images for the period from 2000 to 2010. All data series have been used to calculate standardized precipitation index (SPI) and Normalized Difference Vegetation Index (NDVI). Monthly rainfall data from 12 stations were used to derive the SPI at several time scales (3, 6 and 12-months), the analysis was carried out for the period from 1980 to 2010. Results of the SPI analyses showed that the year 2007-2008 was an extremely drought year for the whole study governorates (Mosul, Kirkuk and Salah Al-Din) with the lowest SPI-12 values −2.67, −2.07 and −2.0 for the three above mentioned governorates, respectively. The results also pointed to the importance of using short time scales in detecting and monitoring the agricultural drought during the crop growing season. The multiple time scales analyzed in this study reflected a clearer picture of the severity and frequencies of drought events, which happened in the study area. The NDVI results were analyzed to get the agricultural drought risk map. The highest NDVI values were 0.33 in 2001, 0.39 in 2003 and 0.20 in 2001 for Mosul, Kirkuk and Salah Al-Din, respectively. While the lowest NDVI values were 0.10 in, 0.19 and 0.13 in 2008 for the three above mentioned governorates respectively. This study emphasized the use of Remote Sensing and GIS in the field of drought risk evaluation. The results showed that the NDVI is an efficient way to monitor changes in vegetation conditions (weekly or daily) during the growing season, and can be used as simple and cost-efficient drought index to monitor agricultural drought at a small or large scale. The NDVI and rainfall were found to be highly correlated 0.83, 0.70 and 0.72 for Mosul, Kirkuk and Salah Al
Mapping of Temporal Variation of Drought using Geospatial Techniques
A drought is 'a protracted period of deficient precipitation resulting in extensive damage to crops, resulting in loss of yield'. Drought always starts with the lack of precipitation, but may affect soil moisture, streams, groundwater, ecosystems and human beings. This leads to the identification of different types of droughts (meteorological, agricultural, hydrological, socioeconomic), which reflect the perspectives of different sectors on water shortages. Geographic Information System (GIS) and Remote Sensing play an important role for near real time monitoring of drought condition over large areas. This study shows temporal variation of drought using temporal image of MODIS NDVI based Enhanced vegetation index (EVI), Vegetation health index (VHI), standard precipitation index (SPI) and Drought severity index (DSI) using software's like Arc-GIS, Google Earth Engine coder, RStudio, SWMM
NDVI and SPI based Drought Assessment of Karur District, Tamil Nadu, India
Indian Geographical Journal, 2015
Drought is a multi-dimensional disaster that plays a deterministic approach in many climatic zones throughout the world. The drought restricts the growth of a region in many ways viz. physically, culturally and economically. The paper discusses the use of geoinformatics in disaster risk management planning, which exists as a very powerful tool in identifying the drought occurrences and in processing the spatial information for better predictions for drought. The drought condition of Karur District, Tamil Nadu, has been considered in this study based on the decreasing trend of rainfall. The data has been compiled from various data repositories such as Open Series Map (OSM) sheets, NRSC Bhuvan portal, and Indian Meteorological Department (IMD). The drought assessment is drawn from two indices, first using the Normalized Difference Vegetation Index (NDVI) for the years 2011 to 2015 and second, with rainfall to generate Standard Precipitation Index (SPI) for the span of 30 years from 1984 to 2013. The drought condition shows a continuous increase from 2012 to 2015 in January months. The drought vulnerability in the study area shows the highest record of very high vulnerability in the villages Paramathi and Tennilai of Aravakurichi Taluk. The map has been classed into four categories viz. very high, high, moderate, and low vulnerable levels. The SPI values and extreme dryness occurred in the years 2000 and 2013. The result also shows that the drought condition prevails over Aravakurichi taluk with dryness with no rainfall for a pronged period. This type of versatile study provides detailed knowledge about climatic based drought assessment, which is helpful to the administrators in making proper plans against disasters like drought.
IEEE Access
Drought is a very complex natural hazard and has a negative impact on the global ecosystem as a whole. Recently Bangladesh has been experiencing by different degree of dryness as a consequence of high climate variability, affecting the crop production to a great extent in the last couple of decades. In this context, the present study was made an effort to assess and analyse drought characteristics based on two drought indices, i.e., Standardized Precipitation Index (SPI) and Vegetation Condition Index (VCI), and model agricultural drought risk with Fast-and-frugal decision tree (FFT) model in Bangladesh from 2001 to 2016. We identified drought occurrence and its dynamics with three-time scale, i.e., SPI3J (November-January), SPI3A (February-April) and SPI6A (November-April), and three rice-growing seasons, i.e., Aus (March-July), Aman (June-November), and Boro (November-May) from TRMM (Tropical Rainfall Measuring Mission) and MODIS (Moderate Resolution Imaging Spectroradiometer) data. The results demonstrate that TRMM had good consistency with rain gauge measurement compared to CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) and PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record) data to derive SPI3J, SPI3A and SPI6A. Overall results confirmed that more drought frequency observed in SPI6A than SPI3J and SPI3A time scale, representing moderate to severe drought throughout the country. Regarding agricultural drought resulting from VCI demonstrated Boro rice-growing season as more vulnerable crop growing season affected by severe to extreme drought event. Validation results of VCI exhibited a high correlation with rice yield data than in-situ soil moisture data. Results of the FFT model show that out of ten predictor variables SPI3J and SPI6A caused agricultural drought with SPI value less than-1.08 and-1.21 respectively. Additionally, the model characterized SPI3J and SPI6A as the most critical driving factors with the highest balanced accuracy triggering agricultural drought risk in Bangladesh.
J Arid Land, 2021
Drought was a severe recurring phenomenon in Iraq over the past two decades due to climate change despite the fact that Iraq has been one of the most water-rich countries in the Middle East in the past. The Iraqi Kurdistan Region (IKR) is located in the north of Iraq, which has also suffered from extreme drought. In this study, the drought severity status in Sulaimaniyah Province, one of four provinces of the IKR, was investigated for the years from 1998 to 2017. Thus, Landsat time series dataset, including 40 images, were downloaded and used in this study. The Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI) were utilized as spectral-based drought indices and the Standardized Precipitation Index (SPI) was employed as a meteorological-based drought index, to assess the drought severity and analyse the changes of vegetative cover and water bodies. The study area experienced precipitation deficiency and severe drought in 1999, 2000, 2008, 2009, and 2012. Study findings also revealed a drop in the vegetative cover by 33.3% in the year 2000. Furthermore, the most significant shrinkage in water bodies was observed in the Lake Darbandikhan (LDK), which lost 40.5% of its total surface area in 2009. The statistical analyses revealed that precipitation was significantly positively correlated with the SPI and the surface area of the LDK (correlation coefficients of 0.92 and 0.72, respectively). The relationship between SPI and NDVI-based vegetation cover was positive but not significant. Low precipitation did not always correspond to vegetative drought; the delay of the effect of precipitation on NDVI was one year.
Assessment of Agricultural Drought Using MODIS Derived Normalized Difference Water Index
2010
Remote sensing index NDVI or its derivatives are used for agricultural drought monitoring and early warning at regional scale worldwide. Studies have shown that NDVI has lagged response to rainfall deficit. Moreover the red band used in NDVI is highly absorbed by crop canopy in comparison to short infrared which has high penetration so thus there remains a discrepancy between the levels of penetration in crop canopy. In contrast, Normalized Difference Water Index (NDWI) uses both the bands in near infrared region and is very sensitive to liquid water content of vegetation canopy and so rainfall. So this study was conducted to evaluate the sensitivity of NDWI in detecting and monitoring the agricultural drought in comparison with NDVI. In the study three indices of NDVI, NDWI5 and NDWI6 were computed using MODIS 09A1 surface reflectance product from June to October of 2002 (drought year) and 2003 (normal year) for the state of Rajasthan. NOAA Climate Prediction Centre (CPC) rainfall product was used and averaged at district level. The NDWI5 showed very strong relation with current rainfall than NDWI6 and weakest was shown by NDVI. The relation of NDVI with lagged rainfall was much better than with current rainfall. The spatial comparison of changes in NDVI and NDWI5 between the drought year (2002) and normal year (2003) for each 8 days composite showed that NDWI5 very well picks up the intensity and extent of drought. Study also showed that NDWI5 is more sensitive to agricultural drought than NDWI6. The study recommends use of NDWI5 for better early detection and monitoring of agricultural drought in operational drought management programmes.