Diagnosis of GLDAS LSM based aridity index and dryland identification

Ghazanfari, Sadegh; Pande, Saket; Hashemy, M.; Sonneveld, B.G.J.S.

doi:10.1016/j.jenvman.2013.01.040

Cited by 24 publications

(9 citation statements)

References 31 publications

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“…The AI data set (Zomer et al, 2007;Zomer et al, 2008) was obtained from the Consultative Group for International Agriculture Research Consortium for Spatial Information. The AI data was developed using the AI, which is the ratio that defines the available rainfall in relation to the evaporative demand of the atmosphere (Feng and Fu, 2013;Ghazanfari et al, 2013). AI is a climate index suitable for assessing drought occurrence and changes in aridity trends, as well as the division of climate regimes (Nastos et al, 2013).…”

Section: Dryland Classificationmentioning

confidence: 99%

Recent changes in global dryland temperature and precipitation

Daramola

2021

Intl Journal of Climatology

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Changes in dryland temperature and precipitation are essential components of dryland climate change. This study applies the use of the monthly reanalysis data product of ERA5, which has a spatial resolution of 0.25 , to investigate the changes in the dryland climate between 1979 and 2018. The result revealed warming and drying of the drylands in recent decades, most notably in the current decade. The general global dryland temperature and precipitation increased and decreased significantly (0.01 significance level) at the rate of 0.032 CÁyear −1 and 0.074 mmÁmonth −1 Áyear −1 , respectively, in the last 40 years. Although precipitation generally decreased over the drylands, summer precipitation increased over southern Africa and Australia as well as northern Africa and south Asia dryland areas. Variation in the warming rate over the dryland zones at different periods was also observed. The semiarid and dry subhumid warmed continually from the first decade (0.02 CÁyear −1 and 0.017 CÁyear −1 ) to the fourth decade (0.077 CÁyear −1 and 0.065 CÁyear −1 , respectively), while the hyperarid warmed at a slower rate in the last two decades compared to the prior decades. More intense warming was also observed in the areas where precipitation decreased, suggesting the effect of reduced evaporative cooling. These changes have implications on the dryland environment, such as aridity changes, which could affect the sustainable development of the areas through the corresponding impact on the ecosystem.

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Section: Dryland Classificationmentioning

confidence: 99%

Recent changes in global dryland temperature and precipitation

Daramola

2021

Intl Journal of Climatology

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“…The climate classification used in this study is based on the aridity index [59], which is expressed as a generalized expression of precipitation, temperature, and potential evapotranspiration (PET), to quantify precipitation availability over atmospheric water demand. It is also expected that different calculation schemes for the aridity index will alter the blended SM product [79,80]. However, a detailed examination of the effect of various schemes of climate classification and aridity index on the blended SM product is beyond the objectives of this paper.…”

Section: Controlling Factors For Sm Blending Over Tpmentioning

confidence: 99%

Blending Satellite Observed, Model Simulated, and in Situ Measured Soil Moisture over Tibetan Plateau

Zeng

Velde

et al. 2016

Remote Sensing

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Abstract:The inter-comparison of different soil moisture (SM) products over the Tibetan Plateau (TP) reveals the inconsistency among different SM products, when compared to in situ measurement. It highlights the need to constrain the model simulated SM with the in situ measured data climatology. In this study, the in situ soil moisture networks, combined with the classification of climate zones over the TP, were used to produce the in situ measured SM climatology at the plateau scale. The generated TP scale in situ SM climatology was then used to scale the model-simulated SM data, which was subsequently used to scale the SM satellite observations. The climatology-scaled satellite and model-simulated SM were then blended objectively, by applying the triple collocation and least squares method. The final blended SM can replicate the SM dynamics across different climatic zones, from sub-humid regions to semi-arid and arid regions over the TP. This demonstrates the need to constrain the model-simulated SM estimates with the in situ measurements before their further applications in scaling climatology of SM satellite products.

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“…GLDAS takes satellite-and ground-based observations and uses land surface modeling and data assimilation techniques to obtain SM, ET, SWE, and other hydrological data. The performance of GLDAS data has been validated in the arid CA (Ghazanfari et al 2013;Tan et al 2018). Combining GRACE and GLDAS data can be used to estimate spatiotemporal changes in GW (△GW), which is also an important part of water storage (Rodell et al 2006;Long et al 2016;Xu et al 2018a).…”

Section: Introductionmentioning

confidence: 99%

The change of hydrological variables and its effects on vegetation in Central Asia

Qing

Wang

Jiang

et al. 2021

Theor Appl Climatol

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Water is an important factor that affects local ecological environments, especially in drylands. The hydrological cycle and vegetation dynamics in Central Asia (CA) have been severely affected by climate change. In this study, we employed data from Gravity Recovery and Climate Experiment (GRACE), Global Land Data Assimilation System (GLDAS), Global Land Evaporation Amsterdam Model, and Climate Research Unit to analyze the spatiotemporal changes in hydrological factors (terrestrial water storage (TWS), evapotranspiration, precipitation, and groundwater) in CA from 2003 to 2015. Additionally, the spatiotemporal changes in vegetation dynamics and the influence of hydrological variables on vegetation were analyzed. The results showed that the declining rates of precipitation, evapotranspiration, GRACE-TWS change, GLDAS-TWS change and GW change were 0.40 mm/year, 0.11 mm/year, 50.46 mm/year (p < 0.05), 8.38 mm/year, and 41.18 mm/year (p < 0.05), respectively. Human activity (e.g., groundwater pumping) was the dominant in determining the GW decline in CA. Precipitation dominated the changes in evapotranspiration, GRACE-TWS and GLDAS-TWS (p < 0.05). The 2- to 3-month lagging signal has to do with the transportation from the ground surface to groundwater. The change in the normalized difference vegetation index (NDVI) from 2003 to 2015 indicated the slight vegetation degradation in CA. The results highlighted that precipitation, terrestrial water storage, and soil moisture make important contributions to the vegetation dynamics changes in CA. The effect of precipitation on vegetation growth in spring was significant (p < 0.05), while the soil moisture effect on vegetation in summer and autumn was higher than that of precipitation.

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Diagnosis of GLDAS LSM based aridity index and dryland identification

Cited by 24 publications

References 31 publications

Recent changes in global dryland temperature and precipitation

Recent changes in global dryland temperature and precipitation

Blending Satellite Observed, Model Simulated, and in Situ Measured Soil Moisture over Tibetan Plateau

The change of hydrological variables and its effects on vegetation in Central Asia

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