Arid
DOI10.3390/w12030630
Improving Princeton Forcing Dataset over Iran Using the Delta-Ratio Method
Zhang, Qinghuan1; Tang, Qiuhong1,2; Liu, Xingcai1; Hosseini-Moghari, Seyed-Mohammad1; Attarod, Pedram3
通讯作者Tang, Qiuhong
来源期刊WATER
EISSN2073-4441
出版年2020
卷号12期号:3
英文摘要In this study, we corrected the bias in the Princeton forcing dataset, i.e., precipitation, maximum and minimum temperatures, and wind speed, by adjusting its long-term mean monthly climatology to match observations for the period 1988-2012 using the delta-ratio method. To this end, we collected meteorological data from 97 stations covering the domain of Iran. We divided Iran into three climatic zones based on the De Martonne classification, i.e., Arid, Humid, and Per-Humid zones, and then applied the delta-ratio method for each climatic zone separately to adjust the bias. After adjustment, the new datasets were compared to the observations in 1958-1987. Results based on four skill scores, including the Nash Sutcliffe efficiency (NSE), percent bias (PBIAS), root-mean-square error (RMSE), and R-2, indicate that the adjustment greatly improved the quality of the gridded dataset, specifically, precipitation, maximum temperature, and wind speed. For example, NSE for annual precipitation during the validation time period increased from -0.03 to 0.72, PBIAS reduced from 29.2% to 6.6%, RMSE decreased by 182.44 mm, and R-2 increased from 0.06 to 0.75. Assessing the results in different climatic zones of Iran reveals that precipitation improved more significantly in the Per-Humid zone followed by the Humid zone, while maximum temperature improved better in the Arid areas. For wind speed, the values improved comparably in the three climate zones. However, the delta values for monthly minimum temperature calculated during the adjustment time period cannot be applied in the validation time period, due to the fact that the Princeton climate data cannot follow the behavior of minimum temperature during the validation phase. In short, we showed that a simple bias adjustment approach, along with minimum observed station data, can significantly improve the performance of global gridded datasets.
英文关键词Iran meteorological forcing data observed data precipitation wind speed
类型Article
语种英语
国家Peoples R China ; Iran
开放获取类型gold
收录类别SCI-E
WOS记录号WOS:000529249500015
WOS关键词REGIONAL CLIMATE MODEL ; PRECIPITATION ; BASIN ; PRODUCTS ; IMPACT
WOS类目Environmental Sciences ; Water Resources
WOS研究方向Environmental Sciences & Ecology ; Water Resources
来源机构中国科学院地理科学与资源研究所
资源类型期刊论文
条目标识符http://119.78.100.177/qdio/handle/2XILL650/318876
作者单位1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China;
2.Univ Chinese Acad Sci, Beijing 100101, Peoples R China;
3.Univ Tehran, Coll Agr & Nat Resources, Fac Nat Resources, Forestry & Forest Econ Dept, Karaj 7787131587, Iran
推荐引用方式
GB/T 7714
Zhang, Qinghuan,Tang, Qiuhong,Liu, Xingcai,et al. Improving Princeton Forcing Dataset over Iran Using the Delta-Ratio Method[J]. 中国科学院地理科学与资源研究所,2020,12(3).
APA Zhang, Qinghuan,Tang, Qiuhong,Liu, Xingcai,Hosseini-Moghari, Seyed-Mohammad,&Attarod, Pedram.(2020).Improving Princeton Forcing Dataset over Iran Using the Delta-Ratio Method.WATER,12(3).
MLA Zhang, Qinghuan,et al."Improving Princeton Forcing Dataset over Iran Using the Delta-Ratio Method".WATER 12.3(2020).
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