Spatiotemporal trends of aridity index in Shiyang River basin of northwest China

Su, Xiaoling; Singh, Vijay P.; Niu, Jiping; Hao, Lina

doi:10.1007/s00477-015-1082-9

Cited by 13 publications

(9 citation statements)

References 29 publications

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“…Aridity is usually expressed as a comprehensive function of precipitation, temperature, and/or potential evapotranspiration, and reflects the level of meteorological drought [38]. AI is defined by Thornthwaite [39], who calculated AI as dividing the difference between precipitation and potential evapotranspiration by the potential evapotranspiration.…”

Section: Aridity Index (Ai Index)mentioning

confidence: 99%

Changes of Reference Evapotranspiration and Its Relationship to Dry/Wet Conditions Based on the Aridity Index in the Songnen Grassland, Northeast China

Zhang

Sun

et al. 2017

Water

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Abstract:Reference evapotranspiration (ET 0 ) plays an important role in regional dry/wet conditions. Based on the Food and Agriculture Organization of the United (FAO) Penman-Monteith method and daily climate variables, ET 0 was calculated for 21 stations in and around the Songnen Grassland, northeast China, during 1960China, during -2014. The temporal and spatial variations of ET 0 and precipitation (P) were analyzed in the annual, seasonal, and growing season (from April to October) time series using the Mann-Kendall test, Sen's slope estimator, and linear regression coupled with a break trend analysis. A sensitivity analysis was used to detect the key climate parameter contributing to ET 0 change. By linear regression analysis on the relationship between ET 0 , P, and the aridity index (AI), the role of ET 0 in determining regional wet/dry conditions was analyzed. Results show a higher ET 0 in the southwest and a lower ET 0 in the northeast, but P was opposite to that of ET 0 . Evident decreasing trends of ET 0 in the annual, seasonal, and growing season time series were detected in almost the entire region by the trend analysis methods. For the entire region, the decreasing trend of ET 0 can be linked to the relative humidity and maximum air temperature. The positive contribution of increasing temperature to ET 0 was offset by the effect of the significantly decreasing relative humidity, wind speed, and sunshine duration at the 0.05 level during 1960-2014. In addition, the value of ET 0 was higher in drought years and lower in wet years.

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Section: Aridity Index (Ai Index)mentioning

confidence: 99%

Changes of Reference Evapotranspiration and Its Relationship to Dry/Wet Conditions Based on the Aridity Index in the Songnen Grassland, Northeast China

Zhang

Sun

et al. 2017

Water

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“…Penman-Monteith method (Eq. 6) was used to estimate ET o because the method is recommended as the sole standard method (Su et al 2015…”

Section: Fao Aridity Indexmentioning

confidence: 99%

Assessment of spatial distribution of aridity indices in Raya valley, northern Ethiopia

2018

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Aridity is a risk with the threat of desertification because of erratic precipitation and prolonged droughts. Aridity indices are often used for the climate-based land classification and monitoring droughts. Hence, this study aimed at analysis of the spatial distribution of aridity indices in Raya valley, northern Ethiopia. Meteorological data from eight meteorological stations located throughout the study area were used to compute the three aridity indices employed: the De Martonne aridity index (I DM), the Pinna combinative index (I P), and the Food and Agriculture Organization aridity index (I FAO). Each aridity index value was computed at each station. Then, once the statistical properties of each aridity index at each station were assessed, inverse distance-weighted geographic information system interpolation technique was used for the spatial estimation of the aridity indices. The three indices indicated a high coefficient of determination which in common revealed the area prone to dry and semi-dry risk. However, I DM was found more appropriate for the considered area since it defines more the climate of each location using seven classes, and it can be computed at specific season and month. The annual, seasonal, and monthly aridity indices of I DM showed high spatial variability of aridity index. The spatial distribution of the I DM underlined irrigation requirements in the northern east part of the study area. This analysis may be helpful for the utilization of water resources and irrigation systems of the study area.

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“…In the arid and semi-arid areas, crop growth mainly depends on agricultural irrigation. Thus, agricultural available water should be taken into consideration, when making decision for suitable and sustainable cultivated land scale [34] . However, it is estimated that the water use efficiency of agricultural irrigation in the middle reaches merely approaches to 0.52 [3,5] .…”

Section: Study Areamentioning

confidence: 99%

Cropping pattern optimization considering uncertainty of water availability and water saving potential

Hao

Singh

2018

International Journal of Agricultural and Biological Engineering

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In arid and semi-arid areas, the profitability of irrigated agriculture mainly depends on the availability of water resources and optimal cropping patterns of irrigation districts. In this study, an integrated agricultural cropping pattern optimization model was developed with considering the uncertainty of water availability and water saving potential in the future, aiming to maximize agricultural net benefit per unit of irrigation water. The available water which was based on the uncertainty of runoff was divided into five scenarios. The irrigation water-saving potential in the future was quantified by assuming an increase in the rate irrigation water-saving of 10% and 20%. The model was applied to the middle reaches of Heihe River basin, in Gansu Province, China. Results showed that if the irrigation water-saving rate was assumed to increase by 10%, then the net water-saving quantity would increase by 21.5-22.5 million m 3 and the gross water-saving quantity would increase by 275.7-303.0 million m 3. Similarly, if the irrigation water-saving rate increased by 20%, then the net water-saving quantity would increase by 43.0-45.1 million m 3 and the gross water-saving quantity would increase by 331.7-383.2 million m 3. If the agricultural cropping pattern was optimized, the optimal water and cultivated area allocation for maize would be greater than those for other crops. Under the premise that similar volume of irrigation water quantity was available in different scenarios, results showed differences in system benefit and net benefit per unit of irrigation water, for the distribution of available irrigation water was diverse in different irrigation districts.

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Spatiotemporal trends of aridity index in Shiyang River basin of northwest China

Cited by 13 publications

References 29 publications

Changes of Reference Evapotranspiration and Its Relationship to Dry/Wet Conditions Based on the Aridity Index in the Songnen Grassland, Northeast China

Changes of Reference Evapotranspiration and Its Relationship to Dry/Wet Conditions Based on the Aridity Index in the Songnen Grassland, Northeast China

Assessment of spatial distribution of aridity indices in Raya valley, northern Ethiopia

Cropping pattern optimization considering uncertainty of water availability and water saving potential

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