Temporal evolution of soil moisture statistical fractal and controls by soil texture and regional groundwater flow

Abstract

Highlights Soil moisture statistical fractal temporal evolution shows seasonal trends and a three-phase event-induced hysteretic patterns  Soil texture is the main cause of hysteresis in fractal temporal evolution; groundwater has important influences that interact with other factors  We separated out the effects of different controls and generalized phenomenological rules that govern fractal evolution AbstractSoil moisture statistical fractal is an important tool for downscaling remotely-sensed observations and has the potential to play a key role in multi-scale hydrologic modeling. The fractal was first introduced two decades ago, but relatively little is known regarding how its scaling exponents evolve in time in response to climatic forcings. Previous studies have neglected the process of moisture re-distribution due to regional groundwater flow. In this study we used a physically-based surface-subsurface processes model and numerical experiments to elucidate the patterns and controls of fractal temporal evolution in two U.S. Midwest basins. Groundwater flow was found to introduce large-scale spatial structure, thereby reducing the scaling exponents ( ), which has implications for the transferability of calibrated parameters to predict . However, the groundwater effects depend on complex interactions with other physical controls such as soil texture and land use. The fractal scaling exponents, while in general showing a seasonal mode that correlates with mean moisture content, display hysteresis after storm events that can be divided into three phases, consistent with literature findings: (a) wetting, (b) re-organizing, and (c) dry-down. Modeling experiments clearly show that the hysteresis is attributed to soil texture, whose "patchiness" is the primary contributing factor. We generalized phenomenological rules for the impacts of rainfall, soil texture, groundwater flow, and land use on evolution. Grid resolution has a mild influence on the results and there is a strong correlation between predictions of from different resolutions. Overall, our results suggest that groundwater flow should be given more consideration in studies of the soil moisture statistical fractal, especially in regions with a shallow water table.

Size Distributions of Arctic Waterbodies Reveal Consistent Relations in Their Statistical Moments in Space and Time

“…The computational constraints of present-day large-scale models preclude the use of information on individual waterbodies, such as their properties and locations. Statistical or reduced-order model representations are therefore used instead to facilitate land-surface observational modeling and data assimilation (Ji et al, 2015;Pau et al, 2016). Under the constraints and limitations discussed above, the variance-mean and skewness-mean relationships provide valuable constraints for generating waterbody size distributions on a subgrid scale and allow the evaluations of hydrological and land surface modeling outcome.…”

“…Waterbody size distributions can also be described by their statistical moments (i.e., the mean, variance, and skewness of the distribution). Statistical moments have been used to identify spatial and temporal variations in many fields of environmental research, e.g., for sediment grain sizes (Folk and Ward, 1957;McLaren and Bowles, 1985), rain droplet sizes (Tapiador et al, 2014), and soil moisture content (Hu et al, 1997;Famiglietti et al, 1998;Brocca et al, 2007;Li and Rodell, 2013;Riley and Shen, 2014;Ji et al, 2015).…”

Temporally stable patterns but seasonal dependent controls of soil water content: Evidence from wavelet analyses

“…Soil water content (SWC) of the surface layer plays a major role in controlling such hydrological processes as run‐off and evapotranspiration over a wide range of spatial scales (Grayson, Western, Chiew, & Blöschl, ; Famiglietti, Rudnicki, & Rodell, ; Wendroth et al, ; Western, Grayson, Blöschl, Willgoose, & McMahon, ; Perry & Niemann, ; Williams, McNamara, & Chandler, ; Ji, Shen, & Riley, ). Intensity of hydrological and biological processes, such as run‐off and evapotranspiration that determine SWC, may vary with landform characteristics.…”

“…This indicated a combined effect of soil and topographic properties on SWC distribution and a clear need for these two factors in developing scale-dependent prediction of SWC in the hummocky landscape of North America. KEYWORDS bivariate wavelet coherency, influencing factor, multiple scales, multiple wavelet coherence, soil moisture, spatial variability 1 | INTRODUCTION Soil water content (SWC) of the surface layer plays a major role in controlling such hydrological processes as run-off and evapotranspiration over a wide range of spatial scales (Grayson, Western, Chiew, & Blöschl, 1997;Famiglietti, Rudnicki, & Rodell, 1998;Wendroth et al, 1999;Western, Grayson, Blöschl, Willgoose, & McMahon, 1999;Perry & Niemann, 2007;Williams, McNamara, & Chandler, 2009;Ji, Shen, & Riley, 2015). Intensity of hydrological and biological processes, such as run-off and evapotranspiration that determine SWC, may vary with landform characteristics.…”

Accurate and efficient prediction of fine‐resolution hydrologic and carbon dynamic simulations from coarse‐resolution models

“…However, these relationships cannot be described in a simple manner [e.g., Das and Mohanty , ; Famiglietti et al ., ; Joshi and Mohanty , ; Mascaro et al ., ; Nykanen and Foufoula‐Georgiou , ]. The soil moisture statistical fractal [ Rodriguez‐Iturbe et al ., ], for example, evolves in time depending on complex interaction between rainfall, groundwater flow, soil water retention, and land‐use heterogeneity [ Ji et al ., ]. A second approach is to explicitly include description of higher‐order moments in the governing equations [ Albertson and Montaldo , ; Montaldo and Albertson , ; Teuling and Troch , ; Kumar , ; Choi et al ., ].…”