Spatial‐temporal variability of soil moisture and its estimation across scales

Abstract

The soil moisture is a quantity of paramount importance in the study of hydrologic phenomena and soil‐atmosphere interaction. Because of its high spatial and temporal variability, the soil moisture monitoring scheme was investigated here both for soil moisture retrieval by remote sensing and in view of the use of soil moisture data in rainfall‐runoff modeling. To this end, by using a portable Time Domain Reflectometer, a sequence of 35 measurement days were carried out within a single year in seven fields located inside the Vallaccia catchment, central Italy, with area of 60 km2. Every sampling day, soil moisture measurements were collected at each field over a regular grid with an extension of 2000 m2. The optimization of the monitoring scheme, with the aim of an accurate mean soil moisture estimation at the field and catchment scale, was addressed by the statistical and the temporal stability. At the field scale, the number of required samples (NRS) to estimate the field‐mean soil moisture within an accuracy of 2%, necessary for the validation of remotely sensed soil moisture, ranged between 4 and 15 for almost dry conditions (the worst case); at the catchment scale, this number increased to nearly 40 and it refers to almost wet conditions. On the other hand, to estimate the mean soil moisture temporal pattern, useful for rainfall‐runoff modeling, the NRS was found to be lower. In fact, at the catchment scale only 10 measurements collected in the most “representative” field, previously determined through the temporal stability analysis, can reproduce the catchment‐mean soil moisture with a determination coefficient, R2, higher than 0.96 and a root‐mean‐square error, RMSE, equal to 2.38%. For the “nonrepresentative” fields the accuracy in terms of RMSE decreased, but similar R2 coefficients were found. This insight can be exploited for the sampling in a generic field when it is sufficient to know an index of soil moisture temporal pattern to be incorporated in conceptual rainfall‐runoff models. The obtained results can address the soil moisture monitoring network design from which a reliable soil moisture temporal pattern at the catchment scale can be derived.

Analysis of spatial variability of near-surface soilmoisture to increase rainfall-runoff modellingaccuracy in SW Hungary

“…For the Pósa Valley, the Number of Representative Stations (NRS) did not significantly changed with increasing soil moisture content (Figure 8). This finding contradicts the conclusion of [38]. The value of NRS varied between 2 and 30 per 100 km 2 in the Pósa Valley when AE was changed to values between 0.5 and 2% (Figure 8).…”

“…Note that our results differed from those of [2], in the way that NRS values change only slightly as a function of soil moisture content. According to [38], when based on field and statistical analyses, the number of monitoring stations can be optimized. In their field site the optimal number of monitoring station was found to be between 4 and 40 depending on saturation levels.…”

Soil moisture dynamics of typical ecosystems in response to precipitation: A monitoring-based analysis of hydrological service in the Qilian Mountains

“…Wang et al, 2013); yet continuous observations of soil moisture dynamics of different ecosystems are still need to be strengthened . Knowledge of soil moisture is critical for developing an understanding of numerous hydrological processes in soil hydrology, meteorology, and ecology research (Brocca et al, 2010;Green and Erskine, 2004;He et al, 2012). Soil moisture is regarded as the basis of quantitative research on hydrological dynamics and ecological patterns/processes (Rodriguez-Iturbel, 2000), participating as crony in the terrestrial hydrological cycle across the "lithosphere-biosphere-atmosphere-hydrosphere" and affecting the growth and succession of vegetation.…”

Linking the soil moisture distribution pattern to dynamic processes along slope transects in the Loess Plateau, China

“…Some studies have found increases in the standard deviation (SD) of soil moisture with reductions in the mean moisture content (Brocca et al 2007;Hupet and Vanclooster 2002), while others have found the opposite (Famiglietti et al 1998;Martinez-Fernandez and Ceballos 2003). Recent studies have found that the SD and mean moisture content display a convex upward relationship (Brocca et al 2010;Gao et al 2011).…”