Spatial and Decadal Variations in Potential Evapotranspiration of China Based on Reanalysis Datasets during 1982–2010

Yao, Yunjun; Zhao, Shaohua; Zhang, Yuhu; Jia, Kun; Liu, Meng

doi:10.3390/atmos5040737

Cited by 36 publications

(31 citation statements)

References 60 publications

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“…This deficit in water supply needs to be compensated by other water resources and/or following good water management and rationalization methods. Similar trends of PET appear in many places in the world, such as China [43]. Overall, the analysis of drought-relevant climatic parameters for both regions is proving notable decreasing levels in precipitation.…”

Section: Climate Descriptive Analysissupporting

confidence: 63%

Drought Forecasting Using Stochastic Models in a Hyper-Arid Climate

Mossad

Alazba

2015

Atmosphere

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Drought forecasting plays a crucial role in drought mitigation actions. Thus, this research deals with linear stochastic models (autoregressive integrated moving average (ARIMA)) as a suitable tool to forecast drought. Several ARIMA models are developed for drought forecasting using the Standardized Precipitation Evapotranspiration Index (SPEI) in a hyper-arid climate. The results reveal that all developed ARIMA models demonstrate the potential ability to forecast drought over different time scales. In these models, the p, d, q, P, D and Q values are quite similar for the same SPEI time scale. This is in correspondence with autoregressive (AR) and moving average (MA) parameter estimate values, which are also similar. Therefore, the ARIMA model (1, 1, 0) (2, 0, 1) could be considered as a general model for the Al Qassim region. Meanwhile, the ARIMA model (1, 0, 3) (0, 0, 0) at 3-SPEI and the ARIMA model (1, 1, 1) (2, 0, 1) at 24-SPEI could be generalized for the Hail region. The ARIMA models at the 24-SPEI time scale is the best forecasting models with high R 2 (more than 0.9) and lower values of RMSE and MAE, while they are the least forecasting at the 3-SPEI time scale. Accordingly, this study recommends that ARIMA models can be very useful tools for drought forecasting that can help water resource managers and planners to take precautions considering the severity of drought in advance. OPEN ACCESSAtmosphere 2015, 6 411

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Section: Climate Descriptive Analysissupporting

confidence: 63%

Drought Forecasting Using Stochastic Models in a Hyper-Arid Climate

Mossad

Alazba

2015

Atmosphere

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“…The aridity index (PET/P ), which is an indicator for the degree of dryness, slightly declined (not significantly) in all basins in the northwestern TP. Although both P and PET increased in the Keliya River basin since the 1980s (Shi et al, 2003;Yao et al, 2014), the PET/P declined due to the higher rates of the increase in P than that in PET. The climate moistening (Shi et al, 2003) in the headwaters of these inland rivers would be beneficial to the water resources and oasis agro-ecosystems in the middle and lower basins.…”

Section: Trends Of Basin-wide Water Budget Components For the Tp Basinsmentioning

confidence: 99%

Investigating water budget dynamics in 18 river basins across the Tibetan Plateau through multiple datasets

Liu

Sun

et al. 2018

Hydrol. Earth Syst. Sci.

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Abstract. The dynamics of basin-scale water budgets over the Tibetan Plateau (TP) are not well understood nowadays due to the lack of in situ hydro-climatic observations. In this study, we investigate the seasonal cycles and trends of water budget components (e.g. precipitation P , evapotranspiration ET and runoff Q) in 18 TP river basins during the period 1982-2011 through the use of multi-source datasets (e.g. in situ observations, satellite retrievals, reanalysis outputs and land surface model simulations). A water balancebased two-step procedure, which considers the changes in basin-scale water storage on the annual scale, is also adopted to calculate actual ET. The results indicated that precipitation (mainly snowfall from mid-autumn to next spring), which are mainly concentrated during June-October (varied among different monsoons-impacted basins), was the major contributor to the runoff in TP basins. The P , ET and Q were found to marginally increase in most TP basins during the past 30 years except for the upper Yellow River basin and some sub-basins of Yalong River, which were mainly affected by the weakening east Asian monsoon. Moreover, the aridity index (PET/P ) and runoff coefficient (Q/P ) decreased slightly in most basins, which were in agreement with the warming and moistening climate in the Tibetan Plateau. The results obtained demonstrated the usefulness of integrating multi-source datasets to hydrological applications in the data-sparse regions. More generally, such an approach might offer helpful insights into understanding the water and energy budgets and sustainability of water resource management practices of data-sparse regions in a changing environment.

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“…In this study, the FAO Penman-Monteith (PM) method driven by meteorological reanalysis data and NDVI3g data was used to estimate PET of Northeast China, which was validated to all basins of China in previous studies [48,49]. The PET estimation method can be expressed as…”

Section: Potential Et Estimationmentioning

confidence: 99%

Satellite-Derived Spatiotemporal Variations in Evapotranspiration over Northeast China during 1982–2010

et al. 2017

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Evapotranspiration (ET) is a critical process for the climate system and water cycles. However, the spatiotemporal variations in terrestrial ET over Northeast China over the past three decades calculated from sparse meteorological point-based data remain large uncertain. In this paper, a recently proposed modified satellite-based Priestley-Taylor (MS-PT) algorithm was applied to estimate ET of Northeast China during 1982-2010. Validation results show that the square of the correlation coefficients (R 2 ) for the six flux tower sites varies from 0.55 to 0.88 (p < 0.01), and the mean root mean square error (RMSE) is 0.92 mm/d. The ET estimated by MS-PT has an annual mean of 441.14 ± 18 mm/year in Northeast China, with a decreasing trend from southeast coast to northwest inland. The ET also shows in both annual and seasonal linear trends over Northeast China during 1982-2010, although this trend seems to have ceased after 1998, which increased on average by 12.3 mm per decade pre-1998 (p < 0.1) and decreased with large interannual fluctuations post-1998. Importantly, our analysis on ET trends highlights a large difference from previous studies that the change of potential evapotranspiration (PET) plays a key role for the change of ET over Northeast China. Only in the western part of Northeast China does precipitation appear to be a major controlling influence on ET.

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Spatial and Decadal Variations in Potential Evapotranspiration of China Based on Reanalysis Datasets during 1982–2010

Cited by 36 publications

References 60 publications

Drought Forecasting Using Stochastic Models in a Hyper-Arid Climate

Drought Forecasting Using Stochastic Models in a Hyper-Arid Climate

Investigating water budget dynamics in 18 river basins across the Tibetan Plateau through multiple datasets

Satellite-Derived Spatiotemporal Variations in Evapotranspiration over Northeast China during 1982–2010

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