Simulation and verification of land surface soil temperatures in the Xinjiang Region by the CLM3.5 model forced by CLDAS

Xianyong, 孟现勇 Meng; Hao, 王浩 Wang; Zhihui, 刘志辉 Liu; Chunxiang, 师春香 Shi; Shiyin, 刘时银 Liu; Xi, 陈曦 Chen; Weiwei, 龚伟伟 Gong

doi:10.5846/stxb201508171717

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“…CLDAS assimilation system fuses multi-source data such as satellite observation, land surface observation and numerical products (Meng et al, 2017, Shi et al, 2008, Shi et al, 2011, Zhang et al, 2013. This study built CMADS dataset (temporal resolution: day by day; spatial resolution: 1/3°; time scale:…”

Section: Introduction Of Cmads Datasetmentioning

confidence: 99%

The China Meteorological Assimilation Driving Datasets for the SWAT Model (CMADS) Application in China: A Case Study in Heihe River Basin

Meng

Wang

Cai

et al. 2017

Preprint

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Section: Introduction Of Cmads Datasetmentioning

confidence: 99%

The China Meteorological Assimilation Driving Datasets for the SWAT Model (CMADS) Application in China: A Case Study in Heihe River Basin

Meng

Wang

Cai

et al. 2017

Preprint

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“…CMADS is a new dataset developed by this study, which is based on CLDAS data assimilation technology. CLDAS assimilation system fuses multi-source data such as satellite observation, land surface observation and numerical products (Meng et al, 2017, Shi et al, 2008, Shi et al, 2011. This study built CMADS dataset (temporal resolution: day by day; spatial resolution: 1/3°; time scale: 2008-2013) by using data loop nesting, resampling and bilinear interpolation methods.…”

Section: Introduction Of Cmads Datasetmentioning

confidence: 99%

The China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS) and it's Application in the Heihe River Basin in China

Wang¹,

Meng²,

Cai³

et al. 2016

Preprint

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Large-scale hydrological modeling in China is challenging given the sparse meteorological stations and large uncertainties associated with atmospheric forcing data. Here we introduce the development and use of the China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS) in the Heihe River Basin(HRB) for improving hydrologic modeling, by leveraging the datasets from the China Meteorological Administration Land Data Assimilation System (CLDAS)(including climate data from nearly 40000 area encryption stations, 2700 national automatic weather stations, FengYun (FY) 2 satellite and radar stations). CMADS uses the Space Time Multiscale Analysis System (STMAS) to fuse data based on ECWMF ambient field and ensure data accuracy. In addition, compared with CLDAS, CMADS includes relative humidity and climate data of varied resolutions to drive hydrological models such as the Soil and Water Assessment Tool (SWAT) model. Here, we compared climate data from CMADS, Climate Forecast System Reanalysis (CFSR) and traditional weather station (TWS) climate forcing data and evaluated their applicability for driving large scale hydrologic modeling with SWAT. In general, CMADS has higher accuracy than CFRS when evaluated against observations at TWS; CMADS also provides spatially continuous climate field to drive distributed hydrologic models, which is an important advantage over TWS climate data, particular in regions with sparse weather stations. Therefore, SWAT model simulations driven with CMADS and TWS achieved similar performances in terms of monthly and daily stream flow simulations, and both of them outperformed CFRS. For example, for the three hydrological stations (Ying Luoxia, Qilian Mountain, and ZhaMasheke) in the HRB at the monthly and daily Nash-Sutcliffe efficiency ranges of 0.75-0.95 and 0.58-0.78, respectively, which are much higher than corresponding efficiency statistics achieved with CFSR (monthly: 0.32-0.49 and daily: 0.26 – 0.45). The CMADS dataset is available free of charge and is expected to a valuable addition to the existing climate reanalysis datasets for deriving distributed hydrologic modeling in China and other countries in East Asia.

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Simulation and verification of land surface soil temperatures in the Xinjiang Region by the CLM3.5 model forced by CLDAS

Cited by 2 publications

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The China Meteorological Assimilation Driving Datasets for the SWAT Model (CMADS) Application in China: A Case Study in Heihe River Basin

The China Meteorological Assimilation Driving Datasets for the SWAT Model (CMADS) Application in China: A Case Study in Heihe River Basin

The China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS) and it's Application in the Heihe River Basin in China

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