Investigating spatiotemporal changes of the land-surface processes in Xinjiang using high-resolution CLM3.5 and CLDAS: Soil temperature

Abstract

Soil temperature plays a key role in the land surface processes because this parameter affects a series of physical, chemical, and biological processes in the soil, such as water and heat fluxes. However, observation of soil temperature is quite limited, especially at the regional scale. Therefore, this study is to investigate the spatiotemporal features of soil temperature in Xinjiang, China, using the Community Land model 3.5 (CLM3.5) with the atmospheric near-surface forcing data of the China Meteorological Administration Land Data Assimilation System (CLDAS). We use the observed soil temperature data collected from 105 national automatic stations during 2009 through 2012 in the study area to verify the simulation capability. The comparison results indicate that the CLM3.5 with the CLDAS driving field could well simulate the spatiotemporal patterns of the soil temperature at hourly, daily, and monthly time scales and at three depths (5 cm, 20 cm, and 80 cm). We also produce a soil temperature database of the region that is continuous both in time and space with high resolution (about 6.25 km). Overall, this study could help understand the regional and vertical characteristics of the soil temperature and provide an important scientific basis for other land-surface processes.

Application of SWAT Model with CMADS Data to Estimate Hydrological Elements and Parameter Uncertainty Based on SUFI-2 Algorithm in the Lijiang River Basin, China

“…Water balance elements in a basin are affected by natural and human factors, such as the types of land use, soil properties [1], geological conditions, glacier [2] and human economic activity [3,4]. It is necessary to study the contribution to the water budget by different hydrological elements in a basin for the purpose of land use management, water resources management, and hydrological process analysis.…”

Significance of the China Meteorological Assimilation Driving Datasets for the SWAT Model (CMADS) of East Asia

“…To address the many aforementioned difficulties, the China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS) [26,27] was developed by Xianyong Meng using STMAS assimilation techniques [20], as well as big data projection and processing methods (including loop nesting of data, projection of resampling models, and bilinear interpolation). CMADS comprises many variables, including daily average temperature, daily maximum temperature, daily minimum temperature, daily cumulative precipitation (20-20 h), daily average relative humidity, daily average specific humidity, daily average solar radiation, daily average wind, daily average atmospheric pressure, soil temperature, and soil moisture.…”

“…In this process, various assimilation techniques and multi-source data (e.g., observation stations, radar stations, satellite remote sensing data, aerial data, and model data) have been widely used to establish atmospheric reanalysis datasets at various scales. Examples include the National Centers for Environmental Prediction/National Center for Atmospheric Research NCEP/NCAR-(R1) reanalysis dataset and National Centers for Environmental Prediction-Department of Energy (NCEP-DOE)-(R2) reanalysis dataset [13,14], Climate Forecast System Reanalysis (CFSR) by NCEP [15], European Centre for Medium-Range Weather Forecasts (ECMWF) 15-year Re-Analysis (ERA-15) [16], ECMWF Re-Analysis from September 1957 to August 2002 (ERA-40) [17], ECMWF Reanalysis-Interim (ERA-Interim) [18], Japanese 25-year Reanalysis project (JRA-25) [19], and the CMA Land Data Assimilation System (CLDAS) [20]. These reanalysis data sets provide important basic data for global researchers to analyse climate-water cycles.…”

Spring Flood Forecasting Based on the WRF-TSRM Mode

“…The meteorological observation stations are scarce in this area [17]. The Juntanghu River is a small river in the western part of the Tianshan Mountains, which originates from the north slope of the mountains.…”