scholarly journals Drought Model DISS Based on the Fusion of Satellite and Meteorological Data under Variable Climatic Conditions

2020 ◽  
Vol 12 (18) ◽  
pp. 2944
Author(s):  
Katarzyna Dabrowska-Zielinska ◽  
Alicja Malinska ◽  
Zbigniew Bochenek ◽  
Maciej Bartold ◽  
Radoslaw Gurdak ◽  
...  
Keyword(s):  
Environmental Conditions ◽  
Land Surface ◽  
Drought Index ◽  
Extreme Values ◽  
Meteorological Data ◽  
Climatic Conditions ◽  
Agricultural Drought ◽  
Drought Monitoring ◽  
Large Area

The use of effective methods for large-area drought monitoring is an important issue; hence, there have been many attempts to solve this problem. In this study, the Drought Information Satellite System (DISS) index is presented, based on the synergistic use of meteorological data and information derived from satellite images. The index allows us to monitor drought phenomena in various climatic and environmental conditions. The approach utilizes two indices for constructing a drought index: (1) the hydrothermal coefficient (HTC), which characterizes meteorological conditions across the study area over a long-term period; and (2) the temperature condition index (TCI) derived from Moderate-resolution Imaging Spectroradiometer (MODIS) data, which refers instantaneous land surface temperature (LST) to long-term extreme values. The model for drought assessment based on the DISS index was applied for generating drought index maps for Poland for the 2001–2019 vegetation seasons. The performance of the index was verified through comparison of the extent of agricultural drought to the reduction in cereal and maize yield. Analysis of variance revealed a significant relationship between the area of drought determined by the drought index and the decrease in cereal yield due to unfavorable growth conditions. The presented study proves that the proposed drought index can be an effective tool for large-area drought monitoring under variable environmental conditions.

scholarly journals Development of Integrated Crop Drought Index by Combining Rainfall, Land Surface Temperature, Evapotranspiration, Soil Moisture, and Vegetation Index for Agricultural Drought Monitoring

2021 ◽  
Vol 13 (9) ◽  
pp. 1778
Author(s):  
Soo-Jin Lee ◽  
Nari Kim ◽  
Yangwon Lee
Keyword(s):  
Soil Moisture ◽  
Surface Temperature ◽  
Crop Yield ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Drought Index ◽  
Vegetation Index ◽  
The State ◽  
Agricultural Drought ◽  
Drought Monitoring

Various drought indices have been used for agricultural drought monitoring, such as Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), Palmer Drought Severity Index (PDSI), Soil Water Deficit Index (SWDI), Normalized Difference Vegetation Index (NDVI), Vegetation Health Index (VHI), Vegetation Drought Response Index (VegDRI), and Scaled Drought Condition Index (SDCI). They incorporate such factors as rainfall, land surface temperature (LST), potential evapotranspiration (PET), soil moisture content (SM), and vegetation index to express the meteorological and agricultural aspects of drought. However, these five factors should be combined more comprehensively and reasonably to explain better the dryness/wetness of land surface and the association with crop yield. This study aims to develop the Integrated Crop Drought Index (ICDI) by combining the weather factors (rainfall and LST), hydrological factors (PET and SM), and a vegetation factor (enhanced vegetation index (EVI)) to better express the wet/dry state of land surface and healthy/unhealthy state of vegetation together. The study area was the State of Illinois, a key region of the U.S. Corn Belt, and the quantification and analysis of the droughts were conducted on a county scale for 2004–2019. The performance of the ICDI was evaluated through the comparisons with SDCI and VegDRI, which are the representative drought index in terms of the composite of the dryness and vegetation elements. The ICDI properly expressed both the dry and wet trend of the land surface and described the state of the agricultural drought accompanied by yield damage. The ICDI had higher positive correlations with the corn yields than SDCI and VegDRI during the crucial growth period from June to August for 2004–2019, which means that the ICDI could reflect the agricultural drought well in terms of the dryness/wetness of land surface and the association with crop yield. Future work should examine the other factors for ICDI, such as locality, crop type, and the anthropogenic impacts, on drought. It is expected that the ICDI can be a viable option for agricultural drought monitoring and yield management.

scholarly journals Integrating effective drought index (EDI) and remote sensing derived parameters for agricultural drought assessment and prediction in Bundelkhand region of India

2014 ◽  
Vol XL-8 ◽  
pp. 89-100 ◽  
Author(s):  
S. K. Padhee ◽  
B. R. Nikam ◽  
S. P. Aggarwal ◽  
V. Garg
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Land Surface ◽  
Drought Index ◽  
Meteorological Data ◽  
Vegetation Indices ◽  
Agricultural Drought ◽  
Predictive Capability ◽  
Drought Assessment ◽  
Soil Moisture Anomaly

Drought is an extreme condition due to moisture deficiency and has adverse effect on society. Agricultural drought occurs when restraining soil moisture produces serious crop stress and affects the crop productivity. The soil moisture regime of rain-fed agriculture and irrigated agriculture behaves differently on both temporal and spatial scale, which means the impact of meteorologically and/or hydrological induced agriculture drought will be different in rain-fed and irrigated areas. However, there is a lack of agricultural drought assessment system in Indian conditions, which considers irrigated and rain-fed agriculture spheres as separate entities. On the other hand recent advancements in the field of earth observation through different satellite based remote sensing have provided researchers a continuous monitoring of soil moisture, land surface temperature and vegetation indices at global scale, which can aid in agricultural drought assessment/monitoring. Keeping this in mind, the present study has been envisaged with the objective to develop agricultural drought assessment and prediction technique by spatially and temporally assimilating effective drought index (EDI) with remote sensing derived parameters. The proposed technique takes in to account the difference in response of rain-fed and irrigated agricultural system towards agricultural drought in the Bundelkhand region (The study area). <br><br> The key idea was to achieve the goal by utilizing the integrated scenarios from meteorological observations and soil moisture distribution. EDI condition maps were prepared from daily precipitation data recorded by Indian Meteorological Department (IMD), distributed within the study area. With the aid of frequent MODIS products viz. vegetation indices (VIs), and land surface temperature (LST), the coarse resolution soil moisture product from European Space Agency (ESA) were downscaled using linking model based on Triangle method to a finer resolution soil moisture product. EDI and spatially downscaled soil moisture products were later used with MODIS 16 days NDVI product as key elements to assess and predict agricultural drought in irrigated and rain-fed agricultural systems in Bundelkhand region of India. Meteorological drought, soil moisture deficiency and NDVI degradation were inhabited for each and every pixel of the image in GIS environment, for agricultural impact assessment at a 16 day temporal scale for Rabi seasons (October&ndash;April) between years 2000 to 2009. Based on the statistical analysis, good correlations were found among the parameters EDI and soil moisture anomaly; NDVI anomaly and soil moisture anomaly lagged to 16 days and these results were exploited for the development of a linear prediction model. The predictive capability of the developed model was validated on the basis of spatial distribution of predicted NDVI which was compared with MODIS NDVI product in the beginning of preceding Rabi season (Oct&ndash;Dec of 2010).The predictions of the model were based on future meteorological data (year 2010) and were found to be yielding good results. The developed model have good predictive capability based on future meteorological data (rainfall data) availability, which enhances its utility in analyzing future Agricultural conditions if meteorological data is available.

DROUGHT OCCURRENCE UNDER LITHUANIAN CLIMATIC CONDITIONS

2015 ◽  
Author(s):  
Laima TAPARAUSKIENĖ ◽  
Veronika LUKŠEVIČIŪTĖ
Keyword(s):  
Meteorological Data ◽  
Standardized Precipitation Index ◽  
Vegetation Period ◽  
Climatic Conditions ◽  
Precipitation Record ◽  
Calculation Step ◽  
Drought Conditions ◽  
The Standardized Precipitation Index ◽  
June July

This study provides the analysis of drought conditions of vegetation period in 1982-2014 year in two Lithuanian regions: Kaunas and Telšiai. To identify drought conditions the Standardized Precipitation Index (SPI) was applied. SPI was calculated using the long-term precipitation record of 1982–2014 with in-situ meteorological data. Calculation step of SPI was taken 1 month considering only vegetation period (May, June, July, August, September). The purpose of investigation was to evaluate the humidity/aridity of vegetation period and find out the probability of droughts occurrence under Lithuanian climatic conditions. It was found out that according SPI results droughts occurred in 14.5 % of all months in Kaunas region and in 15.8 % in Telšiai region. Wet periods in Kaunas region occurred in 15.8 %, and in Telšiai region occurrence of wet periods was – 18.8 % from all evaluated months. According SPI evaluation near normal were 69.7 % of total months during period of investigation in Kaunas and respectively – 65.5 % in Telšiai. The probability for extremely dry period under Lithuania climatic conditions are pretty low – 3.0 % in middle Lithuania and 2.4 % in western part of Lithuania.

scholarly journals Different Drought Legacies of Rain-Fed and Irrigated Croplands in a Typical Russian Agricultural Region

2020 ◽  
Vol 12 (11) ◽  
pp. 1700
Author(s):  
Yuanhuizi He ◽  
Fang Chen ◽  
Huicong Jia ◽  
Lei Wang ◽  
Valery G. Bondur
Keyword(s):  
Surface Temperature ◽  
Drought Index ◽  
Growth Period ◽  
Meteorological Drought ◽  
Agricultural Drought ◽  
Drought Monitoring ◽  
Growth Stages ◽  
Drought Conditions ◽  
Irrigated Lands ◽  
Meteorological Droughts

Droughts are one of the primary natural disasters that affect agricultural economies, as well as the fire hazards of territories. Monitoring and researching droughts is of great importance for agricultural disaster prevention and reduction. The research significance of investigating the hysteresis of agricultural to meteorological droughts is to provide an important reference for agricultural drought monitoring and early warnings. Remote sensing drought monitoring indices can be employed for rapid and accurate drought monitoring at regional scales. In this paper, the Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices and the surface temperature product are used as the data sources. Calculating the temperature vegetation drought index (TVDI) and constructing a comprehensive drought disaster index (CDDI) based on the crop growth period allowed drought conditions and spatiotemporal evolution patterns in the Volgograd region in 2010 and 2012 to be effectively monitored. The causes of the drought were then analyzed based on the sensitivity of a drought to meteorological factors in rain-fed and irrigated lands. Finally, the lag time of agricultural to meteorological droughts and the hysteresis in different growth periods were analyzed using statistical analyses. The research shows that (1) the main drought patterns in 2010 were spring droughts from April to May and summer droughts from June to August, and the primary drought patterns in 2012 were spring droughts from April to June, with an affected area that reached 3.33% during the growth period; (2) local drought conditions are dominated by the average surface temperature factor. Rain-fed lands are sensitive to the temperature and are therefore prone to summer droughts. Irrigated lands are more sensitive to water shortages in the spring and less sensitive to extremely high temperature conditions; (3) there is a certain lag between meteorological and agricultural droughts during the different growth stages. The strongest lag relationship was found in the planting stage and the weakest one was found in the dormancy stage. Therefore, the meteorological drought index in the growth period has a better predictive ability for agricultural droughts during the appropriately selected growth stages.

scholarly journals Evaluating the Spatiotemporal Characteristics of Agricultural Drought in Bangladesh Using Effective Drought Index

Water ◽  
2019 ◽  
Vol 11 (12) ◽  
pp. 2437 ◽  
Author(s):  
Mohammad Kamruzzaman ◽  
Syewoon Hwang ◽  
Jaepil Cho ◽  
Min-Won Jang ◽  
Hanseok Jeong
Keyword(s):  
Central Region ◽  
Drought Index ◽  
Regional Analysis ◽  
Mitigation Strategies ◽  
Agricultural Drought ◽  
Monthly Precipitation ◽  
Effective Drought Index ◽  
Spatiotemporal Characteristics ◽  
North Western

This study aims to assess the spatiotemporal characteristics of agricultural droughts in Bangladesh during 1981–2015 using the Effective Drought Index (EDI). Monthly precipitation data for 36 years (1980–2015) obtained from 27 metrological stations, were used in this study. The EDI performance was evaluated for four sub-regions over the country through comparisons with historical drought records identified by regional analysis. Analysis at a regional level showed that EDI could reasonably detect the drought years/events during the study period. The study also presented that the overall drought severity had increased during the past 35 years. The characteristics (severity and duration) of drought were also analyzed in terms of the spatiotemporal evolution of the frequency of drought events. It was found that the western and central regions of the country are comparatively more vulnerable to drought. Moreover, the southwestern region is more prone to extreme drought, whereas the central region is more prone to severe droughts. Besides, the central region was more prone to extra-long-term droughts, while the coastal areas in the southwestern as well as in the central and north-western regions were more prone to long-term droughts. The frequency of droughts in all categories significantly increased during the last quinquennial period (2011 to 2015). The seasonal analysis showed that the north-western areas were prone to extreme droughts during the Kharif (wet) and Rabi (dry) seasons. The central and northern regions were affected by recurring severe droughts in all cropping seasons. Further, the most significant increasing trend of the drought-affected area was observed within the central region, especially during the pre-monsoon (March–May) season. The results of this study can aid policymakers in the development of drought mitigation strategies in the future.

scholarly journals On the utility of land surface models for agricultural drought monitoring

2012 ◽  
Vol 16 (9) ◽  
pp. 3451-3460 ◽  
Author(s):  
W. T. Crow ◽  
S. V. Kumar ◽  
J. D. Bolten
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Cross Correlation ◽  
Root Zone ◽  
Vegetation Indices ◽  
Agricultural Drought ◽  
Drought Monitoring ◽  
Global Evaluation ◽  
Land Surface Models ◽  
Surface Models

Abstract. The lagged rank cross-correlation between model-derived root-zone soil moisture estimates and remotely sensed vegetation indices (VI) is examined between January 2000 and December 2010 to quantify the skill of various soil moisture models for agricultural drought monitoring. Examined modeling strategies range from a simple antecedent precipitation index to the application of modern land surface models (LSMs) based on complex water and energy balance formulations. A quasi-global evaluation of lagged VI/soil moisture cross-correlation suggests, when globally averaged across the entire annual cycle, soil moisture estimates obtained from complex LSMs provide little added skill (< 5% in relative terms) in anticipating variations in vegetation condition relative to a simplified water accounting procedure based solely on observed precipitation. However, larger amounts of added skill (5–15% in relative terms) can be identified when focusing exclusively on the extra-tropical growing season and/or utilizing soil moisture values acquired by averaging across a multi-model ensemble.

scholarly journals STUDY ON REMOTE SENSING MONITORING MODEL OF AGRICULTURAL DROUGHT BASED ON RANDOM FOREST DEVIATION CORRECTION

2021 ◽  
pp. 413-422
Author(s):  
Shao Li ◽  
Xia Xu
Keyword(s):  
Remote Sensing ◽  
Random Forest ◽  
Remote Sensing Data ◽  
Agricultural Drought ◽  
Data Sources ◽  
Drought Monitoring ◽  
Monitoring Methods ◽  
Large Area ◽  
Sensing Data ◽  
Monitoring Model

Using remote sensing data to monitor large area drought is one of the important methods of drought monitoring at present. However, the traditional remote sensing drought monitoring methods mainly focus on monitoring single drought response factors such as soil moisture or vegetation status, and the research on comprehensive multi-factor drought monitoring is limited. In order to improve the ability to resist drought events, this paper takes Henan Province of China as an example, takes multi-source remote sensing data as data sources, considers various disaster-causing factors, adopts random forest method to model, and explores the method of regional remote sensing comprehensive drought monitoring using various remote sensing data sources. Compared with neural network, classification regression tree and linear regression, the performance of random forest is more stable and tolerant to noise and outliers. In order to provide a new method for comprehensive assessment of regional drought, a comprehensive drought monitoring model was established based on multi-source remote sensing data, which comprehensively considered the drought factors such as soil water stress, vegetation growth status and meteorological precipitation profit and loss in the process of drought occurrence and development.

scholarly journals Spatial Relationship between Precipitation and Runoff in Africa

2018 ◽  
Author(s):  
Fidele Karamage ◽  
Yuanbo Liu ◽  
Xingwang Fan ◽  
Meta Francis Justine ◽  
Guiping Wu ◽  
...  
Keyword(s):  
Land Surface ◽  
Spatial Scales ◽  
Spatial Relationship ◽  
Resource Planning ◽  
Climatic Conditions ◽  
Runoff Coefficient ◽  
Global Precipitation Climatology Centre ◽  
High Runoff ◽  
Monthly Runoff

Abstract. Lack of sufficient and reliable hydrological information is a key hindrance to water resource planning and management in Africa. Hence, the objective of this research is to examine the relationship between precipitation and runoff at three spatial scales, including the whole continent, 25 major basins and 55 countries. For this purpose, the long-term monthly runoff coefficient (Rc) was estimated using the long-term monthly runoff data (R) calculated from the Global Runoff Data Centre (GRDC) streamflow records and Global Precipitation Climatology Centre (GPCC) precipitation datasets for the period of time spanning from 1901 to 2017. Subsequently, the observed Rc data were interpolated in order to estimate Rc over the ungauged basins under guidance of key runoff controlling factors, including the land-surface temperature (T), precipitation (P) and potential runoff coefficient (Co) inferred from the land use and land cover, slope and soil texture information. The results show that 16 % of the annual mean precipitation (672.52 mm) becomes runoff (105.72 mm), with a runoff coefficient of 0.16, and the remaining 84 % (566.80 mm) evapotranspirates over the continent during 1901–2017. Spatial analysis reveals that the precipitation–runoff relationship varies significantly among different basins and countries, mainly dependent on climatic conditions and its inter-annual variability. Generally, high runoff depths and runoff coefficients are observed over humid tropical basins and countries with high precipitation intensity compared to those located in subtropical and temperate drylands.

Methodologies for Evaluating and Qualifying External Pipeline Coatings for Northern Pipelines

2006 ◽  
Author(s):  
S. Papavinasam ◽  
A. Doiron ◽  
T. Panneerselvam ◽  
Y. Lafrenie`re ◽  
M. Attard ◽  
...  
Keyword(s):  
Environmental Conditions ◽  
Low Temperatures ◽  
Laboratory Experiments ◽  
Field Experiments ◽  
Climatic Conditions ◽  
Limited Data ◽  
The North

The design of coatings must be adequate to protect pipelines under long-term, severe environmental conditions, including the extreme climatic conditions that will apply in the North before the pipe is installed and operation begins. Practices and standardised methodologies for evaluating and qualifying pipeline coatings for application in northern pipelines are discussed. Results from laboratory and field experiments, carried out under the conditions to which coatings will be exposed during construction, are presented. Based on 1-year laboratory experiments in which samples were exposed to temperatures as low as −45°C and limited data from the field experiments, it is concluded that Canadian Standards Association (CSA) standards CSA Z662, CSA Z245.20 and CSA Z245.21 adequately cover evaluation of coatings for northern pipelines. However, in order to evaluate the effects of low-temperatures, the specimens should be exposed for at least 4 months. Coatings qualified by CSA Z245.21 (System B1 and B2) are less affected from exposure to low-temperatures than those qualified by CSA Z245.21 (System A1) and CSA Z245.20.

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