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

Xianyong Meng,Hao Wang,Chunxiang Shi,Yiping Wu,Xiaonan Ji

doi:10.3390/w10111555

Abstract

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.

Highlights

Many studies have demonstrated the need for a more realistic distribution of surface climate in meteorological analyses, biogeochemical modelling, and hydrological modelling
Let us describe the various raw data from the meteorological stations that were incorporated during the process of establishing the CMADS datasets, the assimilation process for the CMADS assimilation field data, and the post-processing of the CMADS data
Since prior studies on precipitation and solar radiation exist [24,25,27], further verification of these elements was not performed in this study

Summary

Introduction

Many studies have demonstrated the need for a more realistic distribution of surface climate in meteorological analyses, biogeochemical modelling, and hydrological modelling. Examples of such research include flood and large-scale meteorological studies [1], water balance simulations [2,3], agricultural research [4,5], climate research [6,7,8], and hydrological modelling [9,10]. The existing data have been used all over the world, such as in Climate Forecast System Reanalysis (CFSR) [11]; Water 2018, 10, 1555; doi:10.3390/w10111555 www.mdpi.com/journal/water. 2 of 18 [14], ERA-15 [15], and EAR-40 [16] products from the European Centre for Medium-Range Weather Forecasts have been all over the such as in Climate for Forecast System (CFSR).

Methods

Results

Discussion

Conclusion