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.

Drought dynamics and variability over Bundelkhand region of central India: Past, Present and Future

2021 ◽  
Author(s):  
Md Saquib Saharwardi ◽  
Aditya Kumar Dubey ◽  
Pankaj Kumar ◽  
Dmitry V. Sein
Keyword(s):  
Climate Model ◽  
Monsoon Season ◽  
Science And Technology ◽  
Regional Climate ◽  
Standardized Precipitation Index ◽  
Central India ◽  
Severe Drought ◽  
Future Projection ◽  
Russian Science ◽  
Regional Drought

<p>In the present study, an evaluation of the past, present, and future variability of droughts in the Bundelkhand region of Central India are analyzed. Bundelkhand is a severe drought-prone region with intense water stress, where in the last five years four were drought. Therefore, understanding the drivers of drought over the region and its future projection is quite crucial for regional water management. The assessment has been made by analyzing the observational dataset from 1951-2018 to understand the regional drought dynamics. The future projection is made using a multi-model ensemble from a regional climate model over the CORDEX South-Asia domain under the highest emission scenario. The Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) indices are used to understand present drought and its future projection. In addition to this, drought driving parameters like precipitation, temperature, sea-surface temperature wind circulation has been assessed to understand the regional drought dynamics. The composite analysis of drought indicates that the moisture-laden low-level jet from the Arabian Sea branch generally weakened compared to Bay of Bengal branch for monsoon season. Teleconnections of drought over Bundelkhand region shows that nearly half of the droughts are linked to El-Nino events that have become stronger in recent past. The model result reveals that regional climate variability is reasonably captured over the region. In addition, we found increasing drought frequency since the beginning of the 21<sup>st</sup> century. The detailed results from the analysis will be shown briefly in the general assembly.</p><p><strong>Acknowledgement: </strong>This work is jointly supported by the Department of Science and Technology (DST), Govt. of India, grant number DST/INT/RUS/RSF/P-33/G and the Russian Science Foundation (Project No.: 19-47-02015). The first author is also thankful to the Department of Science and Technology (DST), Govt. of India for providing DST INSPIRE fellowship (Grant No. IF160281).</p>

scholarly journals Drought Risk Assessment in the Khushab Region of Pakistan Using Satellite Remote Sensing and Geospatial Methods

2019 ◽  
Vol 10 (1) ◽  
pp. 48-56 ◽  
Author(s):  
Muhammad Khubaib Abuzar ◽  
Muhammad Shafiq ◽  
Syed Amer Mahmood ◽  
Muhammad Irfan ◽  
Tayyaba Khalil ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Social Life ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Standardized Precipitation Index ◽  
Temporal Analysis ◽  
Agricultural Drought ◽  
Natural Phenomenon ◽  
Drought Risk ◽  
Gis And Remote Sensing

Drought is a harmful and slow natural phenomenon that has significant effects on the economy, social life,agriculture and environment of the country. Due to its slow process it is difficult to study this phenomenon. RemoteSensing and GIS tools play a key role in studying different hazards like droughts. The main objective of the study wasto investigate drought risk by using GIS and Remote Sensing techniques in district Khushab, Pakistan. Landsat ETMimages for the year 2003, 2009 and 2015 were utilized for spatial and temporal analysis of agricultural andmeteorological drought. Normalized difference vegetation index (NDVI) Standardized Precipitation Index (SPI) andrainfall anomaly indices were calculated to identify the drought prone areas in the study area. To monitormeteorological drought SPI values were used and NDVI was calculated for agricultural drought. These indices wereintegrated to compute the spatial and temporal drought maps. Three zones; no drought, slight drought and moderatedrought were identified. Final drought map shows that 30.21% of the area faces moderate drought, 28.36% faces slightdrought while nearly 41.3% faces no drought situation. Drought prevalence and severity is present more in the southernpart of Khushab district than the northern part. Most of the northern part is not under any type of drought. Thus, anoverall outcome of this study shows that risk areas can be assessed appropriately by integration of various data sourcesand thereby management plans can be prepared to deal with the hazard.

Drought monitoring in the Ebro basin: comparison of Soil Moisture and Vegetation anomalies

2020 ◽  
Author(s):  
Maria Jose Escorihuela ◽  
Pere Quintana Quintana-Seguí ◽  
Vivien Stefan ◽  
Jaime Gaona
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Prediction Models ◽  
Vegetation Indices ◽  
Standardized Precipitation Index ◽  
Agricultural Drought ◽  
Drought Monitoring ◽  
Drought Indices ◽  
Ebro Basin ◽  
Soil Moisture Data

<p>Drought is a major climatic risk resulting from complex interactions between the atmosphere, the continental surface and water resources management. Droughts have large socioeconomic impacts and recent studies show that drought is increasing in frequency and severity due to the changing climate.</p><p>Drought is a complex phenomenon and there is not a common understanding about drought definition. In fact, there is a range of definitions for drought. In increasing order of severity, we can talk about: meteorological drought is associated to a lack of precipitation, agricultural drought, hydrological drought and socio-economic drought is when some supply of some goods and services such as energy, food and drinking water are reduced or threatened by changes in meteorological and hydrological conditions. 
</p><p>A number of different indices have been developed to quantify drought, each with its own strengths and weaknesses. The most commonly used are based on precipitation such as the precipitation standardized precipitation index (SPI; McKee et al., 1993, 1995), on precipitation and temperature like the Palmer drought severity index (PDSI; Palmer 1965), others rely on vegetation status like the crop moisture index (CMI; Palmer, 1968) or the vegetation condition index (VCI; Liu and Kogan, 1996). Drought indices can also be derived from climate prediction models outputs. Drought indices base on remote sensing based have traditionally been limited to vegetation indices, notably due to the difficulty in accurately quantifying precipitation from remote sensing data. The main drawback in assessing drought through vegetation indices is that the drought is monitored when effects are already causing vegetation damage. In order to address drought in their early stages, we need to monitor it from the moment the lack of precipitation occurs.</p><p>Thanks to recent technological advances, L-band (21 cm, 1.4 GHz) radiometers are providing soil moisture fields among other key variables such as sea surface salinity or thin sea ice thickness. Three missions have been launched: the ESA’s SMOS was the first in 2009 followed by Aquarius in 2011 and SMAP in 2015.</p><p>A wealth of applications and science topics have emerged from those missions, many being of operational value (Kerr et al. 2016, Muñoz-Sabater et al. 2016, Mecklenburg et al. 2016). Those applications have been shown to be key to monitor the water and carbon cycles. Over land, soil moisture measurements have enabled to get access to root zone soil moisture, yield forecasts, fire and flood risks, drought monitoring, improvement of rainfall estimates, etc.</p><p>The advent of soil moisture dedicated missions (SMOS, SMAP) paves the way for drought monitoring based on soil moisture data. Initial assessment of a drought index based on SMOS soil moisture data has shown to be able to precede drought indices based on vegetation by 1 month (Albitar et al. 2013).</p><p>In this presentation we will be analysing different drought episodes in the Ebro basin using both soil moisture and vegetation based indices to compare their different performances and test the hypothesis that soil moisture based indices are earlier indicators of drought than vegetation ones.</p>

scholarly journals A joint probabilistic index for objective drought identification: the case study of Haiti

2020 ◽  
Vol 20 (2) ◽  
pp. 471-487
Author(s):  
Beatrice Monteleone ◽  
Brunella Bonaccorso ◽  
Mario Martina
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Extreme Event ◽  
Risk Mitigation ◽  
Characteristic Curve ◽  
Standardized Precipitation Index ◽  
Early Warning Systems ◽  
Mitigation Strategies ◽  
Agricultural Drought ◽  
Drought Indices

Abstract. Since drought is a multifaceted phenomenon, more than one variable should be considered for a proper understanding of such an extreme event in order to implement adequate risk mitigation strategies such as weather or agricultural indices insurance programmes or disaster risk financing tools. This paper proposes a new composite drought index that accounts for both meteorological and agricultural drought conditions by combining in a probabilistic framework two consolidated drought indices: the standardized precipitation index (SPI) and the vegetation health index (VHI). The new index, called the probabilistic precipitation vegetation index (PPVI), is scalable, transferable all over the globe and can be updated in near real time. Furthermore, it is a remote-sensing product, since precipitation is retrieved from satellite data and the VHI is a remote-sensing index. In addition, a set of rules to objectively identify drought events is developed and implemented. Both the index and the set of rules have been applied to Haiti. The performance of the PPVI has been evaluated by means of a receiver operating characteristic curve and compared to that of the SPI and VHI considered separately. The new index outperformed SPI and VHI both in drought identification and characterization, thus revealing potential for an effective implementation within drought early-warning systems.

scholarly journals Drought Identification and Trend Analysis Using Long-Term CHIRPS Satellite Precipitation Product in Bundelkhand, India

2021 ◽  
Vol 13 (3) ◽  
pp. 1042 ◽  
Author(s):  
Varsha Pandey ◽  
Prashant K Srivastava ◽  
Sudhir K Singh ◽  
George P. Petropoulos ◽  
Rajesh Kumar Mall
Keyword(s):  
Trend Analysis ◽  
Monsoon Season ◽  
Standardized Precipitation Index ◽  
Meteorological Drought ◽  
Hazard Mapping ◽  
Severe Drought ◽  
Drought Management ◽  
Drought Assessment ◽  
Mk Test ◽  
Spatio Temporal

Drought hazard mapping and its trend analysis has become indispensable due to the aggravated impact of drought in the era of climate change. Sparse observational networks with minimal maintenance limit the spatio-temporal coverage of precipitation data, which has been a major constraint in the effective drought monitoring. In this study, high-resolution satellite-derived Climate Hazards Group Infrared Precipitation with Station (CHIRPS) data has been used for computation of Standardized Precipitation Index (SPI). The study was carried out in Bundelkhand region of Uttar Pradesh, India, known for its substantial drought occurrences with poor drought management plans and lack of effective preparedness. Very limited studies have been carried out in assessing the spatio-temporal drought in this region. This study aims to identify district-wide drought and its trend characterization from 1981 to 2018. The run theory was applied for quantitative drought assessment; whereas, the Mann-Kendall (MK) test was performed for trend analysis at seasonal and annual time steps. Results indicated an average of nine severe drought events in all the districts in the last 38 years, and the most intense drought was recorded for the Jalaun district (1983–1985). A significant decreasing trend is observed for the SPI1 (at 95% confidence level) during the post-monsoon season, with the magnitude varying from −0.16 to −0.33 mm/month. This indicates the increasing severity of meteorological drought in the area. Moreover, a non-significant falling trend for short-term drought (SPI1 and SPI3) annually and short- and medium-term drought (SPI1, SPI3, and SPI6) in winter months have been also observed for all the districts. The output of the current study would be utilized in better understanding of the drought condition through elaborate trend analysis of the SPI pattern and thus helps the policy makers to devise a drought management plan to handle the water crisis, food security, and in turn the betterment of the inhabitants.

scholarly journals Drought Risk Assessment in the Khushab Region of Pakistan Using Satellite Remote Sensing and Geospatial Methods

2019 ◽  
Vol 10 (1) ◽  
pp. 48-56
Author(s):  
Muhammad Khubaib Abuzar ◽  
Muhammad Shafiq ◽  
Syed Amer Mahmood ◽  
Muhammad Irfan ◽  
Tayyaba Khalil ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Social Life ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Standardized Precipitation Index ◽  
Temporal Analysis ◽  
Agricultural Drought ◽  
Natural Phenomenon ◽  
Drought Risk ◽  
Gis And Remote Sensing

Drought is a harmful and slow natural phenomenon that has significant effects on the economy, social life,agriculture and environment of the country. Due to its slow process it is difficult to study this phenomenon. RemoteSensing and GIS tools play a key role in studying different hazards like droughts. The main objective of the study wasto investigate drought risk by using GIS and Remote Sensing techniques in district Khushab, Pakistan. Landsat ETMimages for the year 2003, 2009 and 2015 were utilized for spatial and temporal analysis of agricultural andmeteorological drought. Normalized difference vegetation index (NDVI) Standardized Precipitation Index (SPI) andrainfall anomaly indices were calculated to identify the drought prone areas in the study area. To monitormeteorological drought SPI values were used and NDVI was calculated for agricultural drought. These indices wereintegrated to compute the spatial and temporal drought maps. Three zones; no drought, slight drought and moderatedrought were identified. Final drought map shows that 30.21% of the area faces moderate drought, 28.36% faces slightdrought while nearly 41.3% faces no drought situation. Drought prevalence and severity is present more in the southernpart of Khushab district than the northern part. Most of the northern part is not under any type of drought. Thus, anoverall outcome of this study shows that risk areas can be assessed appropriately by integration of various data sourcesand thereby management plans can be prepared to deal with the hazard.

Artificial Intelligence and machine learning model for spatial and temporal prediction of drought in the Colombia Caribbean region.

2021 ◽  
Author(s):  
Daissy Herrera ◽  
Edier Aristizábal
Keyword(s):  
Artificial Intelligence ◽  
Remote Sensing ◽  
Random Forest ◽  
Soil Water ◽  
False Negative ◽  
Standardized Precipitation Index ◽  
Agricultural Drought ◽  
Caribbean Region ◽  
Temporal Prediction ◽  
Tree Classifier

<p>Drought is one of the most critical hydrometeorological phenomena in terms of impacts to society because it affects soil water content, and consequently, crop production and human diets, in some cases under critical conditions, drought produces starving and people migration. Although Colombia is a tropical country, there are areas of the territory that have periods of drought that cause important economic damages such as fires, death loss in cattle, reduction of the capacity to supply water to persons, impacts to agriculture and fish farming.</p><p>Due to recent advances in terms of spatial and temporal resolutions of remote sensing and Artificial Intelligence techniques, it is possible to develop Automatic Learning Models supported on historic information. In this research was built a classifier  Random Forest (RF) and Bagged Decision Tree Classifier (DTC) model to predict, spatial and temporal drought occurrence in Colombia, using remote sensing data as land surface temperature, precipitation, soil water contentl, and evapotranspiration, and macro climatic variables information as ONI, MEI and SOI.  It was used the Standardized Precipitation Index (SPI) with 3-month time scale, that allows identifying agricultural drought events. The results showed that Random Forest provides the best outcomes. In terms of recall and precision, RF produced 0.84 and 0.59 and DTC brought a 0.8 and 0.33, respectively, to predict drought. The above, evidence that models could overestimate the number of times where drought occurs, in contrast with normal or humid conditions. On the other hand, False Positive and False Negative rates are important facts for measuring the development of models. In this case, the FP and FN rates are 7.5% and 2% for RF and 21% and 2.5% for DTC respectively, that means that both models made fewer mistakes predicted real drought events, but had more errors forecasting real normal or humid condition, especially, DTC model. RF can provide a better performance predicting drought and normal/humid conditions in contrast with DTC. The implementation of the developed model can allow governmental entities assessment and monitor agricultural drought over time. Taking, in consequence, actions to mitigate the impacts of droughts in their territories.</p>

scholarly journals A joint probabilistic index for objective drought identification: the case study of Haiti

2019 ◽  
Author(s):  
Beatrice Monteleone ◽  
Brunella Bonaccorso ◽  
Mario Martina
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Extreme Event ◽  
Risk Mitigation ◽  
Standardized Precipitation Index ◽  
Early Warning Systems ◽  
Mitigation Strategies ◽  
Agricultural Drought ◽  
Drought Indices ◽  
Operating Characteristics

Abstract. Since drought is a multifaceted phenomenon, more than one variable should be considered for a proper understanding of such extreme event in order to implement adequate risk mitigation strategies such as weather or agricultural indices insurance programs, or disaster risk financing tools. This paper proposes a new composite drought index that accounts for both meteorological and agricultural drought conditions, by combining in a probabilistic framework two consolidated drought indices: the Standardized Precipitation Index (SPI) and the Vegetation Health Index (VHI). The new index, called Probabilistic Precipitation Vegetation Index (PPVI), is scalable, transferable all over the globe and can be updated in near-real time. Furthermore, it is a remote-sensing product, since precipitation are retrieved from satellite and the VHI is a remote-sensing index. In addition, a set of rules to objectively identify drought events is developed and implemented. Both the index and the set of rules have been applied to Haiti. The performance of PPVI has been evaluated by means of the Receiver Operating Characteristics curve and compared to the ones of SPI and VHI considered separately. The new index outperformed SPI and VHI both in drought identification and characterization, thus revealing potential for an effective implementation within drought early warning systems.

scholarly journals Drought Monitoring Based on Remote Sensing in a Grain-Producing Region in the Cerrado–Amazon Transition, Brazil

Water ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3366
Author(s):  
Mairon Ânderson Cordeiro Correa de Carvalho ◽  
Eduardo Morgan Uliana ◽  
Demetrius David da Silva ◽  
Uilson Ricardo Venâncio Aires ◽  
Camila Aparecida da Silva Martins ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Standardized Precipitation Index ◽  
Meteorological Drought ◽  
Rain Gauge ◽  
Monthly Precipitation ◽  
Indirect Measurements ◽  
Study Region ◽  
Development Cycle ◽  
Historical Series ◽  
The Standardized Precipitation Index

Drought is a natural disaster that affects a country’s economy and food security. The monitoring of droughts assists in planning assertive actions to mitigate the resulting environmental and economic impacts. This work aimed to evaluate the performance of the standardized precipitation index (SPI) using rainfall data estimated by orbital remote sensing in the monitoring of meteorological drought in the Cerrado–Amazon transition region, Brazil. Historical series from 34 rain gauge stations, in addition to indirect measurements of monthly precipitation obtained by remote sensing using the products CHIRPS-2.0, PERSIANN-CDR, PERSIANN-CCS, PERSIANN, GPM-3IMERGMv6, and GPM-3IMERGDLv6, were used in this study. Drought events detected by SPI were related to a reduction in soybean production. The SPI calculated from the historical rain series estimated by remote sensing allowed monitoring droughts, enabling a high detailing of the spatial variability of droughts in the region, mainly during the soybean development cycle. Indirect precipitation measures associated with SPI that have adequate performance for detecting droughts in the study region were PERSIANN-CCS (January), CHIRPS-2.0 (February and November), and GPM-3IMERGMv6 (March, September, and December). The SPI and the use of precipitation data estimated by remote sensing are effective for characterizing and monitoring meteorological drought in the study region.

scholarly journals Integrated Drought Evaluation Index: Considering The Feedback of The Soil Ecological Environment on Wheat

2021 ◽  
Author(s):  
Yanbin Li ◽  
Yuexiong Wang ◽  
Daoxi Li ◽  
Jiawei Guo ◽  
Xuefang Du ◽  
...  
Keyword(s):  
Standardized Precipitation Index ◽  
Reduction Rate ◽  
Agricultural Drought ◽  
Evaluation Index ◽  
Ecological Environment ◽  
Yield Reduction ◽  
Spatial Correlations ◽  
Water Shortages ◽  
Study Region ◽  
Dryness Index

Abstract With the acceleration of climate instability, drought is causing increasing losses that seriously threaten food security in China. In consideration of the feedback of the ecological environment vulnerability on drought, this study selects the temperature vegetation dryness index to evaluate the boundaries of the regional ecological drought index and integrates many factors, such as precipitation, temperature and human activities, from the four aspects of natural disaster risk management — hazard, vulnerability, exposure and resistance—to establish an integrated drought evaluation index for wheat (IDEIW). The results showed that drought was the main reason for the observed decrease in wheat production of Anyang city, as the most severe water shortages occurred during the physiological water demand period of wheat from March to May. Precipitation scarcities were concentrated throughout the north of the study region, where drought was most frequent and severe. There were highly positive spatial correlations between the IDEIW and the annual yield reduction rate of wheat in dry years, whose bivariate Moran's I values reached 0.39, 0.42, 0.31 and 0.38 in the 2002, 2005, 2011 and 2016, respectively; further, the yield reduction rate increased with drought aggregation. This study clearly demonstrates that, in terms of availability, precision and sensitivity, the IDEIW, which is stronger and stabilizing than the temperature vegetation dryness index and the standardized precipitation index, can be used as an important tool to assess and monitor dynamic variations in agricultural drought and provide a new means for the early warning and forecast management of agricultural drought.

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