Semantic Scholar Open Access 2018 63 sitasi

Establishment and Evaluation of the China Meteorological Assimilation Driving Datasets for the SWAT Model (CMADS)

Xianyong Meng Hao Wang C. Shi Yiping Wu Xiaonan Ji

Abstrak

We describe the construction of a very important forcing dataset of average daily surface climate over East Asia—the China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool model (CMADS). This dataset can either drive the SWAT model or other hydrologic models, such as the Variable Infiltration Capacity model (VIC), the Soil and Water Integrated Model (SWIM), etc. It contains several climatological elements—daily maximum temperature (°C), daily average temperature (°C), daily minimum temperature (°C), daily average relative humidity (%), daily average specific humidity (g/kg), daily average wind speed (m/s), daily 24 h cumulative precipitation (mm), daily mean surface pressure (HPa), daily average solar radiation (MJ/m2), soil temperature (K), and soil moisture (mm3/mm3). In order to suit the various resolutions required for research, four versions of the CMADS datasets were created—from CMADS V1.0 to CMADS V1.3. We have validated the source data of the CMADS datasets using 2421 automatic meteorological stations in China to confirm the accuracy of this dataset. We have also formatted the dataset so as to drive the SWAT model conveniently. This dataset may have applications in hydrological modelling, agriculture, coupled hydrological and meteorological modelling, and meteorological analysis.

Topik & Kata Kunci

Penulis (5)

X

Xianyong Meng

H

Hao Wang

C

C. Shi

Y

Yiping Wu

X

Xiaonan Ji

Format Sitasi

Meng, X., Wang, H., Shi, C., Wu, Y., Ji, X. (2018). Establishment and Evaluation of the China Meteorological Assimilation Driving Datasets for the SWAT Model (CMADS). https://doi.org/10.3390/W10111555

Akses Cepat

Lihat di Sumber doi.org/10.3390/W10111555
Informasi Jurnal
Tahun Terbit
2018
Bahasa
en
Total Sitasi
63×
Sumber Database
Semantic Scholar
DOI
10.3390/W10111555
Akses
Open Access ✓