Examining the impact of scale variations on soil moisture downscaling using temporal persistence

Abstract

Soil moisture estimation is an integral component of operational hydrology including flood fore- casting, drought monitoring, catchment management, and groundwater recharge estimation. Typically, soil moisture information is required at variable spatial scales for the above applications. As a result, soil moisture data at one spatial scale are often aggregated to larger areas or disaggregated to smaller areas for a particu- lar application. There is also increasing demand for accurate downscaling of satellite derived data to smaller spatial resolutions. This transition in spatial scale is often fraught with uncertainties because of the high spatio- temporal variability of soil moisture. However, the rank of soil moisture at a particular location in relation to other spatial locations is widely known to be temporally stable at a certain probability. The concept of temporal persistence of soil moisture offers the potential to better estimate soil moisture across spatial scales. The ap- plication of temporal persistence for estimation of soil moisture across spatial scales has not been thoroughly investigated in the literature. As a result, this study examines the relationship between the rank stability of soil moisture and physiographic features including vegetation types and soil texture groups. This investiga- tion provides physiographic explanations for temporal persistence of soil moisture, and therefore facilitate soil moisture estimation across scales. To assess the estimation across scales, soil moisture data for three spatial resolutions of 12- km, 5-km, and 1- km data for the Yanco region in New South Wales, Australia were used. The soil moisture data for the 12-km, 5-km and 1-km resolutions are land surface model estimates obtained from the Joint UK Land Environment Simulator (JULES). The vegetation data set was obtained from the Australian National Dynamic Land Cover Data set (DLCD). The DLCD was generated from a 16-day Enhanced Vegetation Index composite collected at 250-m resolution from Moderate Resolution Imaging Spectro-radiometer (MODIS) for the period from 2000 to 2008. The DLCD has land cover features clustered into 34 ISO classes with descriptions for the structural character of vegetation, ranging from cultivated and managed land covers (crops and pastures) to natural land covers such as closed forest and sparse, open grasslands. The soil texture data set was derived from the Digital Atlas of Australian Soils which was obtained from the Australian Soil Resource Information System (ASRIS). ASRIS provides a digital map of soil types and their descriptions, typical ranges for soil properties for each soil type, morphology, and physical properties of soil profiles. The soil classification in ASRIS has six main textural groups including sands, sandy loams, loams, clay loams, clay, and light clays. Moreover, the meteorological forcing data including short and long wave incoming radiation, air temperature, precipitation, wind speed, pressure, and specific humidity were obtained from the Australian Community Climate Earth-System Simulator - Australian (ACCESS-A) at hourly time step with approximately 12-km spatial resolution. The ACCESS-A precipitation data set was bias corrected using a 5-km gridded precipitation data from the Australian Water Availability Project (AWAP) data obtained through the Australian Bureau of Meteorology. The results identified temporally stable locations between the three resolution pairs with a percent relative difference of about 6% maximum and 0.1% minimum; indicating that the overall difference for downscaling soil moisture across the three resolution pairs is about 3% of the original value. It was found that temporal sta- bility of soil moisture can facilitate downscaling of soil moisture across different spatial scales, with improved accuracy for decreasing size of spatial resolution. Soil texture and vegetation land cover do not provide strong indicators to identify temporally stable locations, with vegetation cover having a higher correlation than soil texture because of its high homogeneity in the Yanco area.