Developing and testing a long-term soil moisture dataset at the catchment scale

Brocca, Luca; Zucco, Graziano; Moramarco, Tommaso; Morbidelli, Renato

doi:10.1016/j.jhydrol.2013.03.029

Cited by 19 publications

(18 citation statements)

References 45 publications

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“…In-situ measurement is a traditional method for directly measuring soil moisture within a few square meters [3][4][5][6][7]. For hydrological studies, it is important to monitor soil moisture at a large scale (e.g., the basin scale) [8]. In recent decades, remote sensing has been widely used to determine large-scale soil moisture because of its spatially consistent view [9].…”

Section: Introductionmentioning

confidence: 99%

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Temporal Variability of Uncertainty in Pixel-Wise Soil Moisture: Implications for Satellite Validation

Feng

Liu

2015

Remote Sensing

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Abstract:In-situ soil moisture was widely used to validate and calibrate the satellite-retrieved data of different footprints. However, it contained unavoidable uncertainty when used as spatial representative. This paper examined the uncertainty in pixel-wise soil moisture designed for satellite validation in the HiWATER project. Two in-situ data sets were used for the examination, which were carefully designed to capture the spatial heterogeneity of soil moisture at different scales. Our results indicated that the pixel-wise uncertainty increased with increasing extent. At a small area, the uncertainty referred to the natural spatial variability of in-situ soil moisture. With respect to a large area, sampling error of spatial soil moisture played an important role, particularly of dry condition. Temporally, the uncertainty was higher during rainfall than that after then. It suggested that in-situ soil moisture could be more spatially representative at a small area after rainfall, valuable for satellite validation. Uncertainty was correlated to soil moisture. It was strongly correlated to spatial mean at a small scale and was to the spatial pattern at a large scale. Results of this study offered some clues to examine the uncertainty of in-situ soil moisture for satellite validation.

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Section: Introductionmentioning

confidence: 99%

“…In addition, the uncertainty may change temporally, which is strongly correlated to soil moisture dynamics [8,24]. The temporal behavior can be illustrated by two indices.…”

Section: Introductionmentioning

confidence: 99%

Temporal Variability of Uncertainty in Pixel-Wise Soil Moisture: Implications for Satellite Validation

Feng

Liu

2015

Remote Sensing

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“…Along a hillslope of 100 m in length in the Chinese Loess Plateau, SWC of 0-0.06 m was measured 136 times from 25 June 2007 to 30 August 2008 by a Delta-T Devices Theta probe (ML2x) at 51 locations (Hu et al, 2011 (Brocca et al, 2012(Brocca et al, , 2013. The GENCAI area was dominated by grassland with a flat topography, in silty clay soils.…”

Section: Study Area and Data Collectionmentioning

confidence: 99%

Estimating spatially distributed soil water content at small watershed scales based on decomposition of temporal anomaly and time stability analysis

Cheng

2016

Hydrol. Earth Syst. Sci.

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Abstract. Soil water content (SWC) is crucial to rainfallrunoff response at the watershed scale. A model was used to decompose the spatiotemporal SWC into a time-stable pattern (i.e., temporal mean), a space-invariant temporal anomaly, and a space-variant temporal anomaly. The spacevariant temporal anomaly was further decomposed using the empirical orthogonal function (EOF) for estimating spatially distributed SWC. This model was compared to a previous model that decomposes the spatiotemporal SWC into a spatial mean and a spatial anomaly, with the latter being further decomposed using the EOF. These two models are termed the temporal anomaly (TA) model and spatial anomaly (SA) model, respectively. We aimed to test the hypothesis that underlying (i.e., time-invariant) spatial patterns exist in the space-variant temporal anomaly at the small watershed scale, and to examine the advantages of the TA model over the SA model in terms of the estimation of spatially distributed SWC. For this purpose, a data set of near surface (0-0.2 m) and root zone (0-1.0 m) SWC, at a small watershed scale in the Canadian Prairies, was analyzed. Results showed that underlying spatial patterns exist in the space-variant temporal anomaly because of the permanent controls of static factors such as depth to the CaCO 3 layer and organic carbon content. Combined with time stability analysis, the TA model improved the estimation of spatially distributed SWC over the SA model, especially for dry conditions. Further application of these two models demonstrated that the TA model outperformed the SA model at a hillslope in the Chinese Loess Plateau, but the performance of these two models in the GENCAI network (∼ 250 km 2 ) in Italy was equivalent. The TA model can be used to construct a high-resolution distribution of SWC at small watershed scales from coarseresolution remotely sensed SWC products.

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“…As the objective in the calibration of these models is RMSE minimization, these tools have not been constructed in the optimal manner for drought prediction. To wit, if a model were designed for droughts, predicting a soil moisture level of 0.03 m 3 . The former case is an error in assessing the severity of drought conditions while the latter is an error in characterizing soils that are clearly wet.…”

Section: Correlationmentioning

confidence: 99%

“…By calibrating the diagnostic soil moisture equation and validating those algorithms during previous years, this work demonstrates the feasibility of such an approach at USCRN stations, thereby extending the soil moisture record. The reverse of this procedure has been performed previously, generating a precipitation time series from soil moisture estimates [2] and extending these results into the past via a hydrologic model [3].…”

Section: Introductionmentioning

confidence: 99%

Extending the soil moisture data record of the U.S. Climate Reference Network (USCRN) and Soil Climate Analysis Network (SCAN)

Coopersmith

Bell

Cosh

2015

Advances in Water Resources

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a b s t r a c tSoil moisture estimates are valuable for hydrologic modeling, drought prediction and management, climate change analysis, and agricultural decision support. However, in situ measurements of soil moisture have only become available within the past few decades with additional sensors being installed each year. Comparing newer in situ resources with older resources, previously required a period of crosscalibration, often requiring several years of data collection. One new technique to improve this issue is to develop a methodology to extend the in situ record backwards in time using a soil moisture model and ancillary available data sets. This study will extend the soil moisture record of the U.S. Climate Reference Network (USCRN) by calibrating a precipitation-driven model during the most recent few years when soil moisture data are available and applying that model backwards temporally in years where precipitation data are available and soil moisture data are not. This approach is validated by applying the technique to the Soil Climate Analysis Network (SCAN) where the same model is calibrated in recent years and validated during preceding years at locations with a sufficiently long soil moisture record. Results suggest that if two or three years of concurrent precipitation and soil moisture time series data are available, the calibrated model's parameters can be applied historically to produce RMSE values less than 0.033 m 3 /m 3 . With this approach, in locations characterized by in situ sensors with short or intermittent data records, a model can now be used to fill the relevant gaps and improve the historical record as well.Published by Elsevier Ltd.

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Developing and testing a long-term soil moisture dataset at the catchment scale

Cited by 19 publications

References 45 publications

Temporal Variability of Uncertainty in Pixel-Wise Soil Moisture: Implications for Satellite Validation

Temporal Variability of Uncertainty in Pixel-Wise Soil Moisture: Implications for Satellite Validation

Estimating spatially distributed soil water content at small watershed scales based on decomposition of temporal anomaly and time stability analysis

Extending the soil moisture data record of the U.S. Climate Reference Network (USCRN) and Soil Climate Analysis Network (SCAN)

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