Effects of drought on plant parameters of different rangeland types in Khansar region, Iran

Hadian, F; Jafari, Reza; Bashari, Hossein; Tarkesh, Mostafa; Clarke, Kenneth

doi:10.1007/s12517-019-4275-6

Cited by 11 publications

(9 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The spatial and temporal impact of droughts on the society and environment is a curial issue in arid and semi-arid regions like Iran. In accordance to the surveyed literature, a very few studies have been conducted which were also limited to the drought impacts on hydrology and agriculture (e.g., streamflow, vegetation cover, crop yield, groundwater level) in different regions of Iran (Vazifehkhah and Kahya, 2018;Aliabad and Shojaei, 2019;Hadian et al, 2019;Manesh et al, 2019). The studies revealed significant impacts of droughts on water resources, agriculture and ecology and thus, farmer's income, national economy and livelihood of people.…”

Section: Discussionmentioning

confidence: 99%

Spatial assessment of meteorological drought features over different climate regions in Iran

Sharafati

Nabaei

Shahid

2019

Intl Journal of Climatology

View full text Add to dashboard Cite

Iran has a predominantly arid and semi‐arid climate where the drought hazards and their variability are the crucial concerns for water resources management. This study assesses the characteristics and trends of meteorological droughts features in different climate regions of Iran. Monthly rainfall data are analysed using Standardized Precipitation Index (SPI) to reconstruct meteorological droughts for different time steps (1, 3, 6, 9 and 12). The drought events are decomposed into three features, namely Severity (S), Duration (D) and Peak (P) to assess their spatial variations in different climate regions. The Mann–Kendall (MK) test is used to detect the trends in drought features in different climate regions. The percentage area (PA) of increasing/decreasing trend in drought features is computed to assess the spatial variations of trends in each region. Besides, SPI is compared with Standardized Precipitation Evapotranspiration Index (SPEI) to understand the importance of evapotranspiration on meteorological droughts. The high correlations between SPI and SPEI indicate that the results obtained using SPI would not vary if a similar analysis is conducted using SPEI. The results obtained using SPI reveal that short‐duration droughts (6 months or less) are more frequent and severe in the northwest and the north along the Caspian plains while the longer‐duration droughts (more than 6 months) are more frequent and severe in the south, southeast and southwest. Overall, the northwest region of Iran is found most susceptible to droughts in terms of S, P and D. Moderate (40% < PA < 60%) or significant (PA > 60%) increase in the drought features are observed in all climate regions for shorter SPI (3 months or less) except northwest, while no drought feature is found to increase significantly for SPI‐12. Overall, the higher increase in drought S, P and D are found in the central arid region and at higher altitudes.

show abstract

Section: Discussionmentioning

confidence: 99%

Spatial assessment of meteorological drought features over different climate regions in Iran

Sharafati

Nabaei

Shahid

2019

Intl Journal of Climatology

View full text Add to dashboard Cite

show abstract

“…It can be obtained at different spatial resolutions, the most common at 500 × 500 m 2 and 250 × 250 m 2 , named Low and Medium Resolution (LR and MR). NDVI shows a good correlation with biomass of different vegetation types in arid and semiarid areas [12,13]. Another useful index is the NDVI anomaly (Z NDVI ).…”

Section: Introductionmentioning

confidence: 99%

Normalized Difference Vegetation Index Temporal Responses to Temperature and Precipitation in Arid Rangelands

et al. 2021

View full text Add to dashboard Cite

Rangeland degradation caused by increasing misuses remains a global concern. Rangelands have a remarkable spatiotemporal heterogeneity, making them suitable to be monitored with remote sensing. Among the remotely sensed vegetation indices, Normalized Difference Vegetation Index (NDVI) is most used in ecology and agriculture. In this paper, we research the relationship of NDVI with temperature, precipitation, and Aridity Index (AI) in four different arid rangeland areas in Spain’s southeast. We focus on the interphase variability, studying time series from 2002 to 2019 with regression analysis and lagged correlation at two different spatial resolutions (500 × 500 and 250 × 250 m2) to understand NDVI response to meteorological variables. Intraseasonal phases were defined based on NDVI patterns. Strong correlation with temperature was reported in phases with high precipitations. The correlation between NDVI and meteorological series showed a time lag effect depending on the area, phase, and variable observed. Differences were found between the two resolutions, showing a stronger relationship with the finer one. Land uses and management affected the NDVI dynamics heavily strongly linked to temperature and water availability. The relationship between AI and NDVI clustered the areas in two groups. The intraphases variability is a crucial aspect of NDVI dynamics, particularly in arid regions.

show abstract

“…Among different satellite indices, the Normalized Difference Vegetation Index (NDVI) is the most widely used vegetation index by rangeland ecologists [ 9 ]. The NDVI reveals a good correlation with biomass of different vegetation types in arid and semi-arid areas [ 10 , 11 , 12 ]. NDVI anomaly (NDVIa, the relative difference of NDVI to long-term averages of the index) is another index proven useful for identifying drought and water stress on vegetation; therefore, it is suited to study arid and semi-arid ecosystems [ 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

Generalized Structure Functions and Multifractal Detrended Fluctuation Analysis Applied to Vegetation Index Time Series: An Arid Rangeland Study

Sanz

Saá-Requejo

Díaz‐Ambrona

et al. 2021

Entropy

View full text Add to dashboard Cite

Estimates suggest that more than 70% of the world’s rangelands are degraded. The Normalized Difference Vegetation Index (NDVI) is commonly used by ecologists and agriculturalists to monitor vegetation and contribute to more sustainable rangeland management. This paper aims to explore the scaling character of NDVI and NDVI anomaly (NDVIa) time series by applying three fractal analyses: generalized structure function (GSF), multifractal detrended fluctuation analysis (MF-DFA), and Hurst index (HI). The study was conducted in four study areas in Southeastern Spain. Results suggest a multifractal character influenced by different land uses and spatial diversity. MF-DFA indicated an antipersistent character in study areas, while GSF and HI results indicated a persistent character. Different behaviors of generalized Hurst and scaling exponents were found between herbaceous and tree dominated areas. MF-DFA and surrogate and shuffle series allow us to study multifractal sources, reflecting the importance of long-range correlations in these areas. Two types of long-range correlation appear to be in place due to short-term memory reflecting seasonality and longer-term memory based on a time scale of a year or longer. The comparison of these series also provides us with a differentiating profile to distinguish among our four study areas that can improve land use and risk management in arid rangelands.

show abstract

Effects of drought on plant parameters of different rangeland types in Khansar region, Iran

Cited by 11 publications

References 55 publications

Spatial assessment of meteorological drought features over different climate regions in Iran

Spatial assessment of meteorological drought features over different climate regions in Iran

Normalized Difference Vegetation Index Temporal Responses to Temperature and Precipitation in Arid Rangelands

Generalized Structure Functions and Multifractal Detrended Fluctuation Analysis Applied to Vegetation Index Time Series: An Arid Rangeland Study

Contact Info

Product

Resources

About