Spatial and temporal variability of the Aridity Index in Greece

Nastos, Panagiotis T.; Politi, Nadia; Kapsomenakis, John

doi:10.1016/j.atmosres.2011.06.017

Cited by 153 publications

(92 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For example, if aridity continues to increase in the future, trees will experience substantially reduced growth (Williams et al, 2010). Moreover, changing aridity may affect the global water balance; have a significant influence on runoff (Arora, 2002); and cause anomalies in the intensity, spatial extent, and frequency of droughts (Nastos et al, 2013). However, the impact of climate change on aridity over the land surface is complex.…”

Section: Introductionmentioning

confidence: 99%

Projections of aridity and its regional variability over China in the mid‐21st century

Yin

et al. 2015

Intl Journal of Climatology

View full text Add to dashboard Cite

ABSTRACT:The effects of aridity on ecosystems and water cycles are pronounced and have received considerable attention. However, aridity changes due to future warming and its regional variability over China remain uncertain. This paper aims to identify the spatiotemporal variations in aridity and its key influencing factors over China in the mid-21st century based on five general circulation models (GCMs) and four representative concentration pathway (RCP) scenarios. An aridity index (AI), defined as the ratio of reference evapotranspiration (ET o ) to precipitation (P), was calculated. We show that the GCM ensemble means are able to reproduce the variation of aridity during the baseline period. Generally, ET o anomalies are consistently positive. Other than for the RCP2.6 low-emission scenario, precipitation and aridity are both projected to increase. There are pronounced regional differences in aridity changes; i.e. wetter across most of western China and drier across most of eastern China in the mid-21st century. Negative AI anomalies in western China can be attributed mainly to the projected increase in precipitation. In eastern China, the AI was higher despite positive precipitation anomalies, due mainly to the greater effect of climate change on increasing atmospheric moisture demand. This suggests that evapotranspiration demand should be incorporated into aridity changes under future warming.

show abstract

Section: Introductionmentioning

confidence: 99%

Projections of aridity and its regional variability over China in the mid‐21st century

Yin

et al. 2015

Intl Journal of Climatology

View full text Add to dashboard Cite

show abstract

“…AI is one of the most important indexes in meteorology, ecology, and agronomy, which is a widely used measure all over the world [48][49][50]. In this research, the aridity index was calculated at point locations for the wheat and maize growing seasons and the annual timescale.…”

Section: Spatial Analysismentioning

confidence: 99%

Spatio-Temporal Analysis of Meteorological Elements in the North China District of China during 1960–2015

Yang

Yin

et al. 2018

Water

View full text Add to dashboard Cite

Abstract:The North China District (NCD) is one of the main grain production regions in China. The double cropping system of irrigation has been leading to the groundwater table decline at the speed of 1-2 m per year. Climate change leads to uncertainty surrounding the future of the NCD agricultural system, which will have great effects on crop yields and crop water demands. In this research, the Meteorological dataset from 54 weather station sites over the period 1960-2015 were collected to quantify the long-term spatial and temporal trends of meteorological data, including daily minimum temperature (T min ), maximum temperature (T max ), precipitation, solar radiation, reference evapotranspiration (ET 0 ), and aridity index (AI). The results show that the long-term wheat and maize growing season and annual average air temperatures (T min and T max ) showed strong north to south increasing trends throughout the NCD. The average annual precipitation was 632.9 mm across the NCD, more than 70% of which was concentrated in the maize growing season. The regional average annual ET 0 was 1026.1 mm, which was 531.2 and 497.4 mm for the wheat and maize growing season, respectively. The regional precipitation decreased from northwest to southeast in each growing season and annual timescale. The funnel areas have lower precipitation and higher ET 0 than the regional average, which may lead to the mining of the groundwater funnel area. The regional average annual AI is 0.63, which lies in the humid class. For temporal analysis, the regional average trends in annual T min , T max , solar radiation, ET 0 , precipitation, and AI were 0.37 • C/10a, 0.15 • C/10a, −0.28 MJ/day/m 2 /10a, −2.98 mm/10a, −12.04 mm/10a, and 0.005/10a, respectively. The increasing trend of temperature and the decreasing trend of solar radiation may have a negative effect on the regional food security. The funnel area AI showed a significant increasing trend for the winter wheat growing season and a decreasing trend for maize, which indicated that more irrigation will be needed for the maize growing season and the winter fallow policy may lead to the increasing trend precipitation being wasted. Analyzing the growing season and the annual meteorological elements of the spatiotemporal trends can help us better understand the influence of climate change on the natural resources and agricultural development in both the past and the future, and will provide us with invaluable information for the modification of cropping patterns to protect the regional and national water and food security.

show abstract

“…(Figure 1). According to the Köppen criteria, the Mediterranean climate is identified by hot, dry, sunny summers and rainy winter season (Csa, Csb) and this is actually very different from the monsoon climate [22]. The climate is defined by mild and rainy winters with warm and dry summers.…”

Section: Introductionmentioning

confidence: 99%

A New Method for Evaporation Modeling: Dynamic Evolving Neural-Fuzzy Inference System

Kişi

Mansouri

2017

Advances in Meteorology

View full text Add to dashboard Cite

Evaporation estimation is very essential for planning and development of water resources. The study investigates the ability of new method, dynamic evolving neural-fuzzy inference system (DENFIS), in modeling monthly pan evaporation. Monthly maximum and minimum temperatures, solar radiation, wind speed, and relative humidity data obtained from two stations located in Turkey are used as inputs to the models. The results of DENFIS method were compared with the classical adaptive neural-fuzzy inference system (ANFIS) by using root mean square error (RMSE), mean absolute relative error (MARE), and Nash-Sutcliffe Coefficient (NS) statistics. Cross validation was applied for better comparison of the models. The results indicated that DENFIS models increased the accuracy of ANFIS models to some extent. RMSE, MARE, and NS of the ANFIS model were increased by 11.13, 11.45, and 6.83% for the Antalya station and 20.11, 12.94%, and 8.29% for the Antakya station using DENFIS.

show abstract

Spatial and temporal variability of the Aridity Index in Greece

Cited by 153 publications

References 42 publications

Projections of aridity and its regional variability over China in the mid‐21st century

Projections of aridity and its regional variability over China in the mid‐21st century

Spatio-Temporal Analysis of Meteorological Elements in the North China District of China during 1960–2015

A New Method for Evaporation Modeling: Dynamic Evolving Neural-Fuzzy Inference System

Contact Info

Product

Resources

About