Spatial distribution and comparison of aridity indices in Extremadura, southwestern Spain

Moral, Francisco J.; Rebollo, Francisco J.; Paniagua, Luis L.; García-Martín, Abelardo; Honorio, Fulgencio

doi:10.1007/s00704-015-1615-7

Cited by 49 publications

(33 citation statements)

References 19 publications

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“…I DM was developed by de Martonne in the 20th century (de Martonne, ). Although I DM is one of the oldest aridity/humidity indices, it is still used worldwide with relevant results for climate classification (Baltas, ; Deniz et al, ; Croitoru et al, ; Nastos et al, ; Tabari and Aghajanloo, ; Moral et al, ; Zolfaghari et al, ). However this index was initially designed to be used for annual values in temperate climate areas, especially in areas located in Europe, but it can be calculated for shorter time periods and expressed as (de Martonne, ; Baltas, ; Croitoru et al, ):

I_{DM} = \frac{\frac{12}{d} P_{i}}{T_{i} + 10}

where d is the duration of the studied period in months, P i is total precipitation (mm), T i is average temperature (°C), and i is each time step.…”

Section: Theoretical Background and Methodologymentioning

confidence: 99%

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Spatiotemporal variations of aridity in Iran using high‐resolution gridded data

Araghi

Martinez

Adamowski

et al. 2018

Intl Journal of Climatology

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Aridity is a permanent feature of climate based on long‐term climatic conditions over a region. Climatic indices are reliable tools to explore climate type, and climatologists have proposed various indices to classify climate and investigate the aridity or humidity in any region. In this study, we examined spatiotemporal variations of aridity in Iran during the last six decades from 1954 to 2013, using the de Martonne aridity index (IDM), which is calculated based on precipitation and temperature. Data used in this study were extracted from the Global Precipitation Climatology Centre and the University of Delaware gridded data sets, respectively. Both data sets have global high‐resolution (0.5° × 0.5°) coverage, and temporally span more than a century of data (from 1901). According to the data obtained from these data sets, more than 80% of Iran has an arid and semi‐arid climate (annually), although the spatial pattern of IDM varies throughout the year. Using the Mann–Kendall test showed a negative significant trend in IDM in 20% of Iran's total area in spring, and less than 7% in the other seasons of the year. Overall, it can be concluded that there were no significant trends in aridity for most parts of Iran during the last six decades.

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I_{DM} = \frac{\frac{12}{d} P_{i}}{T_{i} + 10}

where d is the duration of the studied period in months, P i is total precipitation (mm), T i is average temperature (°C), and i is each time step.…”

Section: Theoretical Background and Methodologymentioning

confidence: 99%

“…Moral et al . () examined data from 81 stations from 1951 to 2010 in southwestern Spain and found an increase in aridity in the typically more humid locations in the north of the region. Zolfaghari et al .…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal variations of aridity in Iran using high‐resolution gridded data

Araghi

Martinez

Adamowski

et al. 2018

Intl Journal of Climatology

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“…Accordingly, it can be considered as a more expressive variable for substituting temperature in water resources management (Mohammed and Scholz 2017a). Analysis of long time series concerning temperature, precipitation and potential evapotranspiration for an area may produce one of the Many researchers have recently carried out extensive studies on trend analysis of climatic parameters such as precipitation (Koutroulis et al, 2011;Beguería et al 2014;Khan et al 2016;Ahmad et al 2018;Asfaw et al 2018), air temperature, meteorological drought (Banimahd and Khalili 2013;Trenberth et al 2014;Moral et al 2016;Deng et al, 2017;Cheng et al 2018;Hazbavi et al 2018;Yue et al 2018) and regional aridity (Hrnjak et al 2014;Djebou 2017;Mohammed and Scholz 2017a;Radaković et al 2018). However, most research has focused on the spatiotemporal distribution of drought, examined potential drought patterns based on results from global and/or regional climate models (Koutroulis et al, 2011;Trenberth et al 2014;Asfaw et al 2018) and investigated the spatial and temporal variation of drought and aridity (Banimahd and Khalili 2013;Liu et al 2015;Moral et al 2016;Deng et al, 2017;Beguería et al 2014) without evaluation of the potential impact of long-term variations and distributions of weather data on the drought and/or aridity at local scale For example, Tabari et al (2012) investigated the rainfall and drought severity without linking it to the variation of the weather regional aridity.…”

Section: Introductionmentioning

confidence: 99%

Climate Variability Impact on the Spatiotemporal Characteristics of Drought and Aridityin Arid and Semi-Arid Regions

Mohammed

Scholz

2019

Water Resour Manage

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Investigating the spatiotemporal distribution of climate data and their impact on the allocation of the regional aridity and meteorological drought, particularly in semi-arid and arid climate, it is critical to evaluate the climate variability effect and propose sufficient adaptation strategies. The coefficient of variation, precipitation concentration index and anomaly index were used to evaluate the climate variability, while the Mann-Kendall and Sen's slope were applied for trend analysis, together with homogeneity tests. The aridity was evaluated using the alpha form of the reconnaissance drought index (Mohammed & Scholz, Water Resour Manag 31(1):531-538, 2017c), whereas drought episodes were predicted by applying three of the commonly used meteorological drought indices, which are the standardised reconnaissance drought index, standardized precipitation index and standardized precipitation evapotranspiration index. The Upper Zab River Basin (UZRB), which is located in the northern part of Iraq and covers a high range of climate variability, has been considered as an illustrative basin for arid and semiarid climatic conditions. There were general increasing trends in average temperature and potential evapotranspiration and decreasing trends in precipitation from the upstream to the downstream of the UZRB. The long-term analysis of climate data indicates that the number of dry years has temporally risen and the basin has experienced succeeding years of drought, particularly after 1994/1995. There was a potential link between drought, aridity and climate variability. Pettitt's, SNHT, Buishand's and von Neumann's homogeneity test results demonstrated that there is an evident alteration in the mean of the drought and aridity between the pre-and post-alteration point (1994).

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“…Thornthwaite (PE) provided the precipitation effectiveness index (PE) based on mean monthly precipitation (mm) and temperature ( ᵒ C) (Mohamed and Mohamed, 2017). De Martonne index considered as one of the most important and most widely used indicators in applied climatology, it is based on the mean annual precipitation (mm) and mean annual temperature( ᵒ C), it is also can be computed on a less time scales (monthly and seasonally) (Hrnjak et al, 2014;Moral et al, 2016). The classifications of these six aridity indices are presented in Table 2.…”

Section: Aridity Indicesmentioning

confidence: 99%

“…It is a function of a continuum of environmental factors including temperature, precipitation, evaporation, and low vegetative cover (Oliver, 2008). Increase aridity, dryness, and desertification are real hazards, affect the conditions of livelihood of the world's population arid area like affecting on agriculture and water supply due to it may be constraining factors for growth the plant and dissemination (Moral et al, 2016;Piri et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Assessment of spatial distributions of some climate indices in Iraq

Al-Zamili¹,

Al-Lami²

2018

jaar

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Aridity is one of the main factors which distinguish the climate of a region and has significant influence on human activities. This study investigated the spatial distribution of the aridity indices to determine the climate conditions in Iraq over the period (1981-2015), depending on the data of the air temperature and rainfall which obtained from 28 stations distributed through Iraq. The used aridity indices are: Lang, Erinc, Emberger, UNEP, De Martonne and Thornthwaite. The spatial distribution was determined using inverse distance weighting (IDW) interpolated method. The results of aridity indices analysis shows that the hyper-arid, arid, and semi-arid categories are predominant with almost (91%) to (100%) of the country’s area. Dry sub-humid, moist sub-humid and humid categories occupies less than (10%) with most of indices at stations of (Arbil, Sulaymaniyah, and Salahaddin). To evaluate the seasonal spatial distributions, De Martonne was utilized. During winter, the climate types ranged from semi-arid to very-humid, while at spring season from arid to humid. Autumn season dominated by arid at (97%) of study area. The summer season was the driest compared with the other seasons. The change point for aridity indices was detected by using the cumulative sum charts (CUSUMs), it is found for the most stations in (1997). Consequently, the spatial distribution for the aridity indices were analyzed through two periods (1981-1997 and 1998-2015), this analysis showed that the arid and hyper-arid areas were increased in the second period compared with the first period with obvious extension toward the north of Iraq.

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Spatial distribution and comparison of aridity indices in Extremadura, southwestern Spain

Cited by 49 publications

References 19 publications

Spatiotemporal variations of aridity in Iran using high‐resolution gridded data

Spatiotemporal variations of aridity in Iran using high‐resolution gridded data

Climate Variability Impact on the Spatiotemporal Characteristics of Drought and Aridityin Arid and Semi-Arid Regions

Assessment of spatial distributions of some climate indices in Iraq

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