scholarly journals How effective is information on soil-landscape units for determining spatio-temporal variability of near-surface soil moisture?

2018 ◽  
Vol 49 (3) ◽  
pp. 174-182 ◽  
Author(s):  
Paolo Nasta ◽  
Benedetto Sica ◽  
Caterina Mazzitelli ◽  
Paola Di Fiore ◽  
Ugo Lazzaro ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Temporal Variability ◽  
Surface Soil ◽  
Controlling Factors ◽  
Surface Soil Moisture ◽  
Near Surface ◽  
Soil Landscape ◽  
Spatio Temporal ◽  
Landscape Units ◽  
Mapping Units

In the last decades, a growing interest in fostering advanced interdisciplinary studies is leading to the establishment of observatories in pilot catchments for long-term monitoring of hydrological variables and fluxes. Nevertheless prior to sensor network installation, this investment necessitates preliminary surveys on key-variables such as near-surface soil moisture in order to prevent risks of erronously distributing sensors by missing sufficient spatial information for understanding hydrological processes within the landatmosphere interactions. The availability of maps describing areas with similar morphological, topographical, soil, and vegetation characteristics enable preliminary surveys to be organized for capturing spatio-temporal variability of soil moisture as best as possible. The soil-landscape classification can be considered as an interesting approach for grouping mapping units with similar hydrological behavior. Therefore, we assume the soil-landscape units as hydrotopes or hydrological similar units. Six transects were established along two hillsides of the Upper Alento River catchment (southern Italy) which is a proper candidate to become a Critical Zone Observatory. In this paper we use a soil-landscape map to infer spatial and temporal dynamics of soil moisture measured along these transects, whereas quantitative analyses were obtained by using multivariate techniques. The effectiveness of available information on soil-landscape mapping units is evaluated with respect to different observed patterns of soil moisture: wetter- and drier-than average observation points belong to agricultural and forested hillslopes, respectively. Soil texture and topographical controlling factors, especially clay content and slope gradient, are found to explain approximately 70% of the observed spatial variations in soil moisture along the forested hillslopes. The spatial structure explained by the environmental controlling factors decreases to 45% in the cases of the agricultural hillslopes mainly due to perturbations induced by grazing and tillage practices.

Characterization of temporal variability in near-surface soil moisture at scales from 1h to 2 weeks

2006 ◽  
Vol 325 (1-4) ◽  
pp. 56-66 ◽  
Author(s):  
Annie-Claude Parent ◽  
François Anctil ◽  
Léon-Étienne Parent
Keyword(s):  
Soil Moisture ◽  
Temporal Variability ◽  
Surface Soil ◽  
Surface Soil Moisture ◽  
Near Surface

scholarly journals The impact of near-surface soil moisture assimilation at subseasonal, seasonal, and inter-annual timescales

2015 ◽  
Vol 19 (12) ◽  
pp. 4831-4844 ◽  
Author(s):  
C. Draper ◽  
R. Reichle
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Surface Soil ◽  
Root Zone ◽  
Surface Model ◽  
Surface Soil Moisture ◽  
Near Surface ◽  
In Situ Observations ◽  
The Impact

Abstract. A 9 year record of Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) soil moisture retrievals are assimilated into the Catchment land surface model at four locations in the US. The assimilation is evaluated using the unbiased mean square error (ubMSE) relative to watershed-scale in situ observations, with the ubMSE separated into contributions from the subseasonal (SMshort), mean seasonal (SMseas), and inter-annual (SMlong) soil moisture dynamics. For near-surface soil moisture, the average ubMSE for Catchment without assimilation was (1.8 × 10−3 m3 m−3)2, of which 19 % was in SMlong, 26 % in SMseas, and 55 % in SMshort. The AMSR-E assimilation significantly reduced the total ubMSE at every site, with an average reduction of 33 %. Of this ubMSE reduction, 37 % occurred in SMlong, 24 % in SMseas, and 38 % in SMshort. For root-zone soil moisture, in situ observations were available at one site only, and the near-surface and root-zone results were very similar at this site. These results suggest that, in addition to the well-reported improvements in SMshort, assimilating a sufficiently long soil moisture data record can also improve the model representation of important long-term events, such as droughts. The improved agreement between the modeled and in situ SMseas is harder to interpret, given that mean seasonal cycle errors are systematic, and systematic errors are not typically targeted by (bias-blind) data assimilation. Finally, the use of 1-year subsets of the AMSR-E and Catchment soil moisture for estimating the observation-bias correction (rescaling) parameters is investigated. It is concluded that when only 1 year of data are available, the associated uncertainty in the rescaling parameters should not greatly reduce the average benefit gained from data assimilation, although locally and in extreme years there is a risk of increased errors.

scholarly journals A Data-Driven Approach for Daily Real-Time Estimates and Forecasts of Near-Surface Soil Moisture

2017 ◽  
Vol 18 (3) ◽  
pp. 837-843 ◽  
Author(s):  
Randal D. Koster ◽  
Rolf H. Reichle ◽  
Sarith P. P. Mahanama
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Real Time ◽  
Surface Soil ◽  
Data Driven ◽  
Surface Soil Moisture ◽  
Near Surface ◽  
Time Estimates ◽  
Data Driven Approach ◽  
Global Surface

Abstract NASA’s Soil Moisture Active Passive (SMAP) mission provides global surface soil moisture retrievals with a revisit time of 2–3 days and a latency of 24 h. Here, to enhance the utility of the SMAP data, an approach is presented for improving real-time soil moisture estimates (nowcasts) and for forecasting soil moisture several days into the future. The approach, which involves using an estimate of loss processes (evaporation and drainage) and precipitation to evolve the most recent SMAP retrieval forward in time, is evaluated against subsequent SMAP retrievals themselves. The nowcast accuracy over the continental United States is shown to be markedly higher than that achieved with the simple yet common persistence approach. The accuracy of soil moisture forecasts, which rely on precipitation forecasts rather than on precipitation measurements, is reduced relative to nowcast accuracy but is still significantly higher than that obtained through persistence.

scholarly journals Influence of cracking clays on satellite estimated and model simulated soil moisture

2010 ◽  
Vol 14 (6) ◽  
pp. 979-990 ◽  
Author(s):  
Y. Y. Liu ◽  
J. P. Evans ◽  
M. F. McCabe ◽  
R. A. M. de Jeu ◽  
A. I. J. M. van Dijk ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Surface Temperature ◽  
Soil Properties ◽  
Surface Soil ◽  
Clay Soils ◽  
Soil Types ◽  
Soil Porosity ◽  
Surface Soil Moisture ◽  
Near Surface ◽  
Community Land Model

Abstract. Vertisols are clay soils that are common in the monsoonal and dry warm regions of the world. One of the characteristics of these soil types is to form deep cracks during periods of extended dry, resulting in significant variation of the soil and hydrologic properties. Understanding the influence of these varying soil properties on the hydrological behavior of the system is of considerable interest, particularly in the retrieval or simulation of soil moisture. In this study we compare surface soil moisture (θ in m3 m−3) retrievals from AMSR-E using the VUA-NASA (Vrije Universiteit Amsterdam in collaboration with NASA) algorithm with simulations from the Community Land Model (CLM) over vertisol regions of mainland Australia. For the three-year period examined here (2003–2005), both products display reasonable agreement during wet periods. During dry periods however, AMSR-E retrieved near surface soil moisture falls below values for surrounding non-clay soils, while CLM simulations are higher. CLM θ are also higher than AMSR-E and their difference keeps increasing throughout these dry periods. To identify the possible causes for these discrepancies, the impacts of land use, topography, soil properties and surface temperature used in the AMSR-E algorithm, together with vegetation density and rainfall patterns, were investigated. However these do not explain the observed θ responses. Qualitative analysis of the retrieval model suggests that the most likely reason for the low AMSR-E θ is the increase in soil porosity and surface roughness resulting from cracking of the soil. To quantitatively identify the role of each factor, more in situ measurements of soil properties that can represent different stages of cracking need to be collected. CLM does not simulate the behavior of cracking soils, including the additional loss of moisture from the soil continuum during drying and the infiltration into cracks during rainfall events, which results in overestimated θ when cracks are present. The hydrological influence of soil physical changes are expected to propagate through the modeled system, such that modeled infiltration, evaporation, surface temperature, surface runoff and groundwater recharge should be interpreted with caution over these soil types when cracks might be present. Introducing temporally dynamic roughness and soil porosity into retrieval algorithms and adding a "cracking clay" module into models are expected to improve the representation of vertisol hydrology.

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