Interannual and seasonal variability of modelled soil moisture in Oklahoma

Abstract

The temporal variability of soil moisture is examined for Oklahoma, where diverse land surface, soils, and precipitation regimes exist. Using readily available meteorological, vegetation, and soils data, a hydrologic model was used to estimate daily soil moisture at 258 evaluation locations for the 30 year period from 1 January 1962 through 31 December 1991. A geographic information system (GIS) was used for the analysis, to take advantage of its powerful analysis capabilities and visualization tools. Results document the long-term average soil moisture conditions, the variability that occurred during this 30 year period and the temporal scale of soil moisture variability for Oklahoma. On average, the soil moisture conditions in both winter and spring in Oklahoma are about 70% of field capacity and decrease to below 30% during the summer. Thus, the seasonal cycle is pronounced, although periods of wetter and drier conditions are evident. Overall, the period from 1962 through to early 1967, 1970-72, and 1976 through to 1981 were drier than the long-term average soil moisture conditions. By contrast, wetter soil moisture conditions prevailed in late 1972 through to early 1975 and 1985 to the early months of 1988, where winter and spring soil moisture values often exceeded the long-term average. An empirical estimate of the temporal consistency in soil moisture over Oklahoma is 53 days (1.8 months), and the amount of variance explained by the soil moisture (red noise) signal is 86%. No significant seasonal differences in the temporal autocorrelation were observed.

The Soil and Water Assessment Tool: Historical Development, Applications, and Future Research Directions

“…Overall, the model was adequate in simulating soil-water patterns for all three watersheds with a daily time step. SWAT was used by Deliberty and Legates (2003) to document (Table 2). Green et al (2006) report a further application of the improved tile drainage routine using SWAT2005 for a large tile-drained watershed in north central Iowa.…”

The Use of C- and L-Band Repeat-Pass Interferometric SAR Coherence for Soil Moisture Change Detection in Vegetated Areas

“…Fluctuations in the surface soil moisture result in variations in the latent and sensible heat fluxes, affecting air temperature and humidity and therefore having a significant influence on the short-term climate of a region [1,2]. On a global scale, the world`s soils contain more carbon than is present in the atmosphere or vegetation [3,4].…”

“…= systemNoise Spatiai Temporal (2) The | System Noise | accounts for distortions due both to the SAR processor, e.g. focusing and misregistration, and due to the thermal noise in the receivers (expressed by the signal-tonoise (SNR) ratio of the system).…”

“…focusing and misregistration, and due to the thermal noise in the receivers (expressed by the signal-tonoise (SNR) ratio of the system). Equation (2) assumes the thermal noise in the receivers is equal. For the repeat-pass configuration, the changes in the viewing geometry or baseline (| Spatial |) and the changes in the surface scattering centres or their relative orientation between acquisitions (| Temporal |) are the main factors affecting the interferometric phase [21].…”

Two-Way Calibration-Validation of SWAT Model for a Small Prairie Watershed with Short Observed Record

“…The Soil Water Assessment Tool (SWAT) (Arnold et al, 1998) is a leading model that belongs to this category. A review article by Gassman et al (2007) pointed out the fundamental strength of SWAT model as a comprehensive package of hydrological and hydraulic processes that allowed its widespread use in the simulation runoff and water yield (Anand et al, 2007;Mekonnen et al, 2009) and soil water content (Deliberty and Legates, 2003;Mapfumo et al, 2004;Narasimhan et al, 2005), the evaluation of the effect of agricultural and structural BMPs on water quality (Stewart et al, 2006;Gollamudi et al, 2007;Gitau et al, 2008) and the assessment of the effectiveness of structural BMPs on reducing soil erosion (Bracmort et al, 2006;Yang et al, 2009). Gassman et al (2007) recognized that SWAT's strength also reflects its weakness in the sense that empirical and semiempirical depictions of some processes may be overly simplified and require further refinement.…”