Spatial distribution of PCI on the basis of four seasons of Pakistan in the years of drought, 2000, 2001, 2002, 2004, 2009, 2010, and 2012

Spatial distribution of PCI on the basis of four seasons of Pakistan in the years of drought, 2000, 2001, 2002, 2004, 2009, 2010, and 2012

Source publication
Article
Full-text available
Drought is one of the deadly natural disasters that leave tearstained faces and broken dreams in its wake. Lifecycle as we know it comes to a halt during a dry season in a region. The purpose of this study was to observe the temporal and spatial variation of droughts in the rain-fed area of Potohar plateau (22,254 km²), Punjab, Pakistan, from 2000...

Context in source publication

Context 1
... spatial distribution and variations of TCI in Fig. 6 are showing the pattern followed by a temperature of land surface throughout the years of drought identified in calculations of SPI and SPEI. Like in temporal plot, TCI is also following a smooth and almost uniform pattern spatially, save for certain changes in the years of the drought. It indicates that TCI has very small hand in ...

Similar publications

Article
Full-text available
The agricultural drought assessment and monitoring has become a prime concern in recent times as it impedes land capability and causes food scarcity. Therefore, the present study constructed a methodological framework through the Google Earth Engine (GEE) platform, which offers advanced and effective monitoring in a timely concern of the drought oc...

Citations

... Moreover, because of the building structuring, wind movement in the urban localities is restricted. So a human body has to face uneasiness and needs cooling of air in the scenario of such temperatures (Khan et al. 2020b). According to Hussain et al. (2022a), economic developmental activity is the key reason for increasing the buildup area in various areas in Okara district. ...
Article
Full-text available
Land surface temperature (LST) is defined as a phenomenon which shows that microclimate of an urban system gets heated much faster than its surrounding rural climates. The expansion of buildings has a noteworthy influence on land use/land cover (LULC) due to conversion of vegetation land into commercial and residential areas and their associated infrastructure by which LST is accelerated. The objective of the research was to study the impact of changes in LULC on LST of Southern Punjab (Pakistan) through remote sensing (RS) data. Landsat images of 30-year duration (1987, 1997, 2007 and 2017) were employed for identifying vegetation indices and LST in the study region. These images also helped to work out normalized difference water index (NDWI) and normalized difference built-up index (NDBI) maps. There was an increase from 29620 (3.63 %) to 88038 ha (10.8 %) in built-up area over the 30 years. LST values were found in the range 12–42 °C, 11–44 °C, 11–45 °C and 11–47 °C in the years 1987, 1997, 2007 and 2017, respectively. Regression coefficients (R²) 0.81, 0.78, 0.84 and 0.76 were observed between NDVI and LST in the corresponding years respectively. Our study showed that NDVI and NDWI were negatively correlated with less LST; however, NDBI showed positive correlation with high LST. Our study gives critical information of LULC and LST and will be a helpful tool for policy makers for developing effective policies in managing land resources.
... Fig. 1 shows the development of some condition indices in recent years. In CI-related drought monitoring applications, such examples of CI variants could be multiplied indefinitely (Ali et al., 2020;Hazaymeh and K. Hassan, 2016;Javed et al., 2020;Khan et al., 2019;Khosravi et al., 2017;Möllmann et al., 2020;Shen et al., 2019;Souza et al., 2021). This phenomenon may be caused by several reasons, such as limited remote sensing data Li et al., 2020), counterproductive modification (Al Zayed et al., 2015), and mistakenly adoption in practical applications (Sur et al., 2020;Tian et al., 2022). ...
Article
Full-text available
Drought condition indices (CIs), including single condition (SCIs) and combined condition indices (CCIs), can reveal the characteristics of drought evolution and have been widely used in drought monitoring tasks. However, in practical applications, CIs are constructed in different normalization methods rather than based on a uniform standard. The diversely constructed CIs not only mislead the scientific community but also confuse farmers and stockholders to prevent them from taking timely measures when drought events occur. Thus, this study aims to comprehensively evaluate the differently constructed CIs and quantify the impact of different normalization strategies. To achieve this, four SCIs, including the Vegetation Condition Index (VCI), the Temperature Condition Index (TCI), the Soil Moisture Condition Index (SMCI), and the Precipitation Condition Index (PCI), together with three CCIs, including the Vegetation Health Index (VHI), the Scaled Drought Condition Index (SDCI), and the Microwave Integrated Drought Index (MIDI) were chosen as typical CI representatives. Each original CI was compared in the Continental United States (CONUS) with its variants by calculating the correlation coefficients with six in-situ indices (PDSI, Zindex, SPI-1, SPI-3, SPI-6, and SPI-9). Furthermore, the different CIs were qualitatively evaluated with the United States Drought Monitor in the extreme drought event in 2011. The results indicated that CI variants built through temporal normalization reduce the accuracy of drought severity estimation, and the CI variants built through spatial normalization lost the ability to track the spatiotemporal evolution of drought. The impacts of different normalization methods on single indices are transmissible and cumulative when integrating them into combined indices. These findings highlight the risks of diverse normalization methods in CIs establishment and underscore the necessity of considering seasonality and spatiality in drought applications.
... Temperature and precipitation are the main climatic factors that produce changes in soil water content and thus modulate vegetation growth and health [59,61]. The NDVI has been frequently used to study vegetation dynamics in arid and semi-arid areas, the degree of aridity, and desertification and to assess meteorological drought due to its relationship with climatic factors (e.g., LST, precipitation) [18,20,[62][63][64]. Significant associations between NDVI-LST and NDVI-precipitation in all hydrological seasons reflect the tight control of vegetation reflectance on the upper layer of soil water content [65]; however, there is wide variability, particularly when the water content is lower, which results in a lowering of the strength of the correlation between seasonal NDVI-LST. ...
Article
Full-text available
Evaluating how meteorological drought affects areas covered by natural ecosystems is challenging due to the lack of ground-based climate data, historical records, and weather station observation with limited coverage. This research tests how the surface reflectance–derived indices (SRDI) may solve this problem by assessing the condition and vegetation dynamics. We use long–term, monthly surface reflectance data (26 hydrological years, 1992/93–2017/18) from Landsat 5 TM, 7 ETM+, and 8 OLI/TIRS satellites and calculated the following five SRDI: Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), Vegetation Health Index (VHI), Normalized Difference Water Index (NDWI), and Modified Soil Adjusted Vegetation Index (MSAVI). The SRDI allows us to detect, classify, and quantify the area affected by drought in the Guadalupe Valley Basin (GVB) via correlations with the Reconnaissance Drought Index (RDI) and the Standardized Precipitation Index (SPI) (weather station-based data). For particular SRDI–RDI and SRDI–SPI combinations, we find positive seasonal correlations during April–May (IS2) and for annual (AN) values (MSAVI IS2–RDI AN, R = 0.90; NDWI IS2–SPI AN, R = 0.89; VHI AN–RDI AN, R = 0.86). The drought–affected GVB area accounted for >87% during 2001/02, 2006/07, 2013/14, and 2017/18. MSAVI and NDWI are the best meteorological drought indicators in this region, and their application minimizes the dependence on the availability of climatic data series.
... This region is believed to originate in the Cambrian era along with the Himalayan mountainous range, which extends through India to Tibet and Nepal (Hughes et al., 2019). Precipitation is relatively high in Rawalpindi and Jhelum districts, while the other districts receive relatively irregular and low rainfall (Rashid and Rasul, 2011;Khan et al., 2020). Temperature range is more or less similar up to moderate elevations, but higher elevations are cooler, often receiving snowfall from December to February. ...
... Physiological attributes responded similarly, where only chlorophyll b increased consistently along the elevation gradient. Root vascular region area, pith area and its cellular area increased at higher elevations, which facilitates water and nutrient conductions, and provides more space for water, carbohydrates and nutrient storage in pith parenchyma (Khan and Khan, 2020). Among stem anatomical attributes, stem cross-sectional area, epidermal cell area, sclerenchymatous thickness, vascular bundle number and area increased at higher elevations (above 1100 m). ...
Article
Full-text available
The adaptability potential of plants enables them to colonize diverse habitats along elevation gradients. Studying these adaptive traits and linking them to environmental attributes provide useful information to understand limitations imposed by elevation gradients on distribution of plant species. To meet these objectives, six most dominant perennial grass species with broad distributional ranges along an elevation gradient from 300-1400 m a.s.l in northern Punjab (Pakistan) were selected for the present study. Dominance of different grass species were linked to proportion of parenchyma, sclerification of aerial plant parts, size of vascular tissue, leaf thickness, and size and density of trichomes. Chrysopogon serrulatus dominated all elevations except the highest one, which was directly linked to increased root area and high proportion of storage parenchyma, vascular region, large metaxylem vessels, increased sclerification in aerial parts, and size and density of trichomes. Cymbopogon jwarancusa dominated lower and middle elevations (300-1000 m) and exhibited increased sclerification in stem and leaves, higher vascular bundle area, and, increased leaf sheath thickness. Lognormal distribution exhibited a non-linear response for eco-morphological and physiological characteristics with decreasing pattern along increase in elevation. Physiological traits responded negatively in response to climatic variables. Root anatomical traits exhibited nonlinear response at lower elevation, while stem traits responded positively at medium ele-vational gradients (700-1000 m). Leaf sheath showed positive response with elevation and temperature. In conclusion, morpho-physiological and anatomical modifications were specific to grasses studied, which contributed differently towards growth and survival along elevation gradient.
... This region is believed to originate in the Cambrian era along with the Himalayan mountainous range, which extends through India to Tibet and Nepal (Hughes et al., 2019). Precipitation is relatively high in Rawalpindi and Jhelum districts, while the other districts receive relatively irregular and low rainfall (Rashid and Rasul, 2011;Khan et al., 2020). Temperature range is more or less similar up to moderate elevations, but higher elevations are cooler, often receiving snowfall from December to February. ...
... Physiological attributes responded similarly, where only chlorophyll b increased consistently along the elevation gradient. Root vascular region area, pith area and its cellular area increased at higher elevations, which facilitates water and nutrient conductions, and provides more space for water, carbohydrates and nutrient storage in pith parenchyma (Khan and Khan, 2020). Among stem anatomical attributes, stem cross-sectional area, epidermal cell area, sclerenchymatous thickness, vascular bundle number and area increased at higher elevations (above 1100 m). ...
Article
The adaptability potential of plants enables them to colonize diverse habitats along elevation gradients. Studying these adaptive traits and linking them to environmental attributes provide useful information to understand limitations imposed by elevation gradients on distribution of plant species. To meet these objectives, six most dominant perennial grass species with broad distributional ranges along an elevation gradient from 300–1400 m a.s.l in northern Punjab (Pakistan) were selected for the present study. Dominance of different grass species were linked to proportion of parenchyma, sclerification of aerial plant parts, size of vascular tissue, leaf thickness, and size and density of trichomes. Chrysopogon serrulatus dominated all elevations except the highest one, which was directly linked to increased root area and high proportion of storage parenchyma, vascular region, large metaxylem vessels, increased sclerification in aerial parts, and size and density of trichomes. Cymbopogon jwarancusa dominated lower and middle elevations (300–1000 m) and exhibited increased sclerification in stem and leaves, higher vascular bundle area, and, increased leaf sheath thickness. Lognormal distribution exhibited a non-linear response for eco-morphological and physiological characteristics with decreasing pattern along increase in elevation. Physiological traits responded negatively in response to climatic variables. Root anatomical traits exhibited nonlinear response at lower elevation, while stem traits responded positively at medium elevational gradients (700–1000 m). Leaf sheath showed positive response with elevation and temperature. In conclusion, morpho-physiological and anatomical modifications were specific to grasses studied, which contributed differently towards growth and survival along elevation gradient.
... Satellite observations are very effective and efficient in the monitoring of natural disasters globally (Verstappen, 1995;Jayaraman et al., 1997;Van Westen, 2000;Hong et al., 2007;Rahamatkar, 2019;Khan et al., 2020;Pham-Duc & Tran, 2020;Sunitha & Avanija, 2020;Adedeji et al., 2020;Orimoloye et al., 2021). These studies have shown the applicability of aerospace technologies in disaster risk reduction and management. ...
Article
Full-text available
Flood incidence, especially in global south countries, is one of the most challenging natural disasters in the light of changing climates, especially in Africa. This is because African countries have a large sub-section of vulnerable people who either live within flood-prone areas or depend on flood-prone areas for their means of livelihood such as we have in Nigeria. Recent flood disasters in Nigeria have been of major concern to people, communities, and institutions. Several studies have been conducted on flood events and their impacts in Nigeria. However, most of these studies are on public perception, flood modeling (rainfall-runoff), and the provision of binary maps with few studies engaging in the use of satellite observations, especially the use of Synthetic Aperture Radar, SAR, to enhance flood early warning designs, especially in Sub-Saharan Africa. This study is aimed at assessing the 2018 flood event in Lokoja, Kogi State, Nigeria, using the Sentinel-1 imagery. The study confirmed that a total of 69 buildings out of 611 buildings were affected by the flood disaster with about 24,902 people displaced by this singular flood event. The study shows that backscattering from microwave sensors provides very useful information for highlighting inundated areas that could prove useful in forecasting, monitoring, and precision-based flood early warning designs before, during, and after flood events.
... TerraClimate utilise la méthode d'interpolation climatique multisource, combinant ainsi les caractéristiques de la haute résolution spatiale de WorldClim et de la haute résolution temporelle de CRU Ts4.0 et de la réanalyse Japonaise de 55 ans (JRA55) (Abatzoglou, et al., 2018). Les données de la base TerraClimate ont été utilisées par Salhi et al. (2019) pour l'étude de la tendance et la distribution des précipitations dans le nord du Maroc, alors que Khan et al. (2020) l'ont utilisé pour l'étude de la sécheresse au Pakistan. ...
Conference Paper
Full-text available
La sécheresse est l’un des risques naturels les plus menaçants au Maroc. Elle peut toucher la compagne agricole à n’importe quel moment de l'année. La fréquence de ce phénomène météorologique et climatique a connu une intensification ces dernières décennies. La surveillance et le suivi de ce phénomène est crucial pour un pays comme le Maroc. Dans ce travail, nous avons choisi d’utiliser deux sources de données afin de suivre l’évolution historique de ce risque. Dans ce sens, nous avons adopté une approche multiindices (six indices) afin de mieux caractériser la sécheresse dans la région de Fès-Meknès. Les indices choisis sont basés exclusivement sur les données des précipitations, il s’agit des indices IPS, CZI, RAI, DI, PNI et ZSI. Ces indices sont calculés à partir des données de stations de mesures et des données de précipitations maillées de TerraClimate durant la période 1982-2017. L’objectif de l'étude est le suivi de la sécheresse dans cinq stations de la région Fès-Meknès. En général, les niveaux de significativité des coefficients de corrélation entre les différents indices calculés à partir des données Terraclimate montre un niveau assez significatif et supérieur à ceux dérivés des données des stations de mesure, avec des valeurs de R qui varient entre 0,89 et 0,99. Tous les indices indiquent l'année 1984 comme l'année la plus sèche, suivie de 2005, 2015 et 2017, alors que les années les plus humides sont 1996 et 2010 pour les deux sources de données. Parmi les indices qui ont enregistré un niveau de corrélation assez élevé, on note IPS avec RAI et Z-score et entre RAI, PNI et Z-score. En se basant sur le haut niveau de corrélation entre les indices de sécheresse dérivés à la fois des données TerraClimate et de celles des stations de mesures, on peut dire que les données Terraclimate peuvent servir pour le suivi de la sécheresse dans la région de Fès-Meknès et à remplir les lacunes dans les données des stations de mesures et même à évaluer le risque de la sécheresse dans les zones non servies par des stations de mesures.
... Some researchers have employed GEE to evaluate the environmental conditions, such as harmful algal blooms (Weber et al., 2020), extract shorelines (Vos et al., 2019), mapping croplands (Bey et al., 2020), monitoring the vegetation expansion in Himalaya (Anderson et al., 2020), drought hazard assessment in Pakistan (Khan et al., 2020), surface urban heat islands (Chakraborty & Lee, 2019), forest loss in Peru (Nicolau et al., 2019), monitoring the irrigation across the USA (Deines et al., 2019), agricultural fire in India (Liu et al., 2019a), wetland inventory (Amani et al., 2019) and air pollutant emissions inventory (Fuentes et al., 2019) in Canada, and for global fire emissions (Liu et al., 2019b). ...
Article
Full-text available
One of the main sources of greenhouse gases is forest fire, with carbon dioxide as its main constituent. With increasing global surface temperatures, the probability of forest fire events also increases. A method that enables rapid quantification of emissions is even more necessary to estimate the environmental impact. This study introduces the application of the Google Earth Engine platform to monitor burned areas in forest fire events in Mount Arjuno, Indonesia, during the 2016–2019 period, using Landsat-8 and Sentinel-2 satellite imageries. The events particularly affected grassland and tropical forest areas, as well as a fraction of agricultural areas, with a total estimated emission of 2.5 × 103 tCO2/km2 burned area. Higher carbon dioxide emissions were also observed, consistent with the higher local surface temperature as well as the CO total column mixing ratio average retrieved from Sentinel-5 p Tropospheric Monitoring Instrument during the period of analysis.
... GEE contains a consolidated resource of open-access RS datasets, along with a variety of algorithms to extract information for Earth's surface monitoring (Amani et al., 2020b(Amani et al., , 2017. These advantages have contributed to GEE's applicability for large-scale drought monitoring (Aksoy et al., 2019;Có rdova et al., 2020;Khan et al., 2020;Okal et al., 2020;Sazib et al., 2018). Aksoy et al. (2019) studied drought severity in Turkey using MODIS imagery to determine various drought indices. ...
... Aksoy et al. (2019) studied drought severity in Turkey using MODIS imagery to determine various drought indices. Khan et al. (2020) employed different data sources to investigate the emergence of drought in the Potohar plateau. Other drought studies using RS data have modeled the spatio-temporal statistical behavior of RSDIs (Oesting and Stein, 2018) and the movement of drought (Rulinda et al., 2013). ...
Article
Remote Sensing (RS) offers efficient tools for drought monitoring, especially in countries with a lack of reliable and consistent in-situ multi-temporal datasets. In this study, a novel RS-based Drought Index (RSDI) named Temperature-Vegetation-soil Moisture-Precipitation Drought Index (TVMPDI) was proposed. To the best of our knowledge, TVMPDI is the first RSDI using four different drought indicators in its formulation. TVMPDI was then validated and compared with six conventional RSDIs including VCI, TCI, VHI, TVDI, MPDI and TVMDI. To this end, precipitation and soil temperature in-situ data have been used. Different time scales of meteorological Standardized Precipitation Index (SPI) index have also been used for the validation of the RSDIs. TVMPDI was highly correlated with the monthly precipitation and soil temperature in-situ data at 0.76 and 0.81 values respectively. The correlation coefficients between the RSDIs and 3-month SPI ranged from 0.07 to 0.28, identifying the TVMPDI as the most suitable index for subsequent analyses. Since the proposed TVMPDI could considerably outperform the other selected RSDIs, all spatiotemporal drought monitoring analyses in Iran were conducted by TVMPDI over the past 21 years. In this study, different products of the Moderate Resolution Imaging Spectrometer (MODIS), Tropical Rainfall Measuring Mission (TRMM), and Global Precipitation Measurement (GPM) datasets containing 15206 images were used on the Google Earth Engine (GEE) cloud computing platform. According to the results, Iran experienced the most severe drought in 2000 with a 0.715 TVMPDI value lasting for almost two years. Conversely, the TVMPDI showed a minimum value equal to 0.6781 in 2019 as the lowest annual drought level. The drought severity and trend in the 31 provinces of Iran have also been mapped. Consequently, various levels of decrease over the 21 years were found for different provinces, while Isfahan and Gilan were the only provinces showing an ascending drought trend (with a 0.004% and 0.002% trendline slope respectively). Khuzestan also faced a worrying drought prevalence that occurred in several years. In summary, this study provides updated information about drought trends in Iran using an advanced and efficient RSDI implemented in the cloud computing GEE platform. These results are beneficial for decision-makers and officials responsible for environmental sustainability, agriculture and the effects of climate change.
... Drought is one of the costliest natural disasters known to mankind (Kogan 1997). It has four types: agriculture drought (crops when impacted by the dryness), meteorological drought (the precipitation recorded less than normal), hydrological drought (low water level in streams, reservoirs, and groundwater), and socio-economic drought (the livelihood and economy getting impacted) (Khan et al. 2020) Droughts do not occur abruptly but rather develops slowly over time (Palmer 1965). Rise and fall in certain climatic or hydro-meteorological indicators (air temperature, humidity, groundwater table, surface runoff, land surface temperature, transpiration, evapotranspiration, soil moisture, precipitation, and heat level, etc.) can become the consequent cause of drought in a region (Svoboda and Fuchs 2017;Khan et al. 2020). ...
... It has four types: agriculture drought (crops when impacted by the dryness), meteorological drought (the precipitation recorded less than normal), hydrological drought (low water level in streams, reservoirs, and groundwater), and socio-economic drought (the livelihood and economy getting impacted) (Khan et al. 2020) Droughts do not occur abruptly but rather develops slowly over time (Palmer 1965). Rise and fall in certain climatic or hydro-meteorological indicators (air temperature, humidity, groundwater table, surface runoff, land surface temperature, transpiration, evapotranspiration, soil moisture, precipitation, and heat level, etc.) can become the consequent cause of drought in a region (Svoboda and Fuchs 2017;Khan et al. 2020). ...
... In this research, the drought indices are computed on Google Earth Engine (GEE); an online cloud-based platform (Gorelick et al. 2017), preliminary used by individuals associated with remote sensing and satellite imagery genre (Moore and Hansen 2011;Kumar and Mutanga 2018;Gorelick et al. 2017;Khan et al. 2020). GEE runs on python/java API and is used for manipulating and performing operations on image or image collections (Moore and Hansen 2011). ...
Article
Full-text available
Drought or dryness occurs due to the accumulative effect of certain climatological and hydrological variables over a certain period. Droughts are studied through numerically computed simple or compound indices. Vegetation condition index (VCI) is used for observing the change in vegetation that causes agricultural drought. Since the land surface temperature has minimum influence from cloud contamination and humidity in the air, so the temperature condition index (TCI) is used for studying the temperature change. Dryness or wetness of soil is a major indicator for agriculture and hydrological drought and for that purpose, the index, soil moisture condition index (SMCI), is computed. The deviation of precipitation from normal is a major cause for meteorological droughts and for that purpose, precipitation condition index (PCI) is computed. The years when the indices escalated the dryness situation to severe and extreme are pointed out in this research. Furthermore, an interactive dashboard is generated in the Google Earth Engine (GEE) for users to compute the said indices using country boundary, time period, and ecological mask of their choice: Agriculture Drought Monitoring. Apart from global results, three case studies of droughts (2002 in Australia, 2013 in Brazil, and 2019 in Thailand) computed via the dashboard are discussed in detail in this research.