Global soil moisture bimodality in satellite observations and climate models

Vilasa, Luis U.; Miralles, Diego G.; Dolman, A. J.

doi:10.1002/2016jd026099

Cited by 21 publications

(26 citation statements)

References 85 publications

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“…The bimodal distribution of soil moisture could be explained by a negative soil moisture and precipitation feedback in the western CONUS and a positive soil moisture and precipitation feedback in the eastern CONUS [68]. Furthermore, areas with soil moisture bimodality have been recognized across global satellite observations and climate models [73]. We identified areas of low agreement between our soil moisture predictions and field stations (lower r 2 values) across the transitional ecosystems (Fig 4) from drier to humid soil moisture environments (i.e., Central Plains and lower Mississippi basin).…”

Section: Discussionmentioning

confidence: 99%

Downscaling satellite soil moisture using geomorphometry and machine learning

Guevara

Vargas

2019

PLoS ONE

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Annual soil moisture estimates are useful to characterize trends in the climate system, in the capacity of soils to retain water and for predicting land and atmosphere interactions. The main source of soil moisture spatial information across large areas (e.g., continents) is satellite-based microwave remote sensing. However, satellite soil moisture datasets have coarse spatial resolution (e.g., 25–50 km grids); and large areas from regional-to-global scales have spatial information gaps. We provide an alternative approach to predict soil moisture spatial patterns (and associated uncertainty) with higher spatial resolution across areas where no information is otherwise available. This approach relies on geomorphometry derived terrain parameters and machine learning models to improve the statistical accuracy and the spatial resolution (from 27km to 1km grids) of satellite soil moisture information across the conterminous United States on an annual basis (1991–2016). We derived 15 primary and secondary terrain parameters from a digital elevation model. We trained a machine learning algorithm (i.e., kernel weighted nearest neighbors) for each year. Terrain parameters were used as predictors and annual satellite soil moisture estimates were used to train the models. The explained variance for all models-years was >70% (10-fold cross-validation). The 1km soil moisture grids (compared to the original satellite soil moisture estimates) had higher correlations (improving from r2 = 0.1 to r2 = 0.46) and lower bias (improving from 0.062 to 0.057 m3/m3) with field soil moisture observations from the North American Soil Moisture Database (n = 668 locations with available data between 1991–2013; 0-5cm depth). We conclude that the fusion of geomorphometry methods and satellite soil moisture estimates is useful to increase the spatial resolution and accuracy of satellite-derived soil moisture. This approach can be applied to other satellite-derived soil moisture estimates and regions across the world.

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Section: Discussionmentioning

confidence: 99%

Downscaling satellite soil moisture using geomorphometry and machine learning

Guevara

Vargas

2019

PLoS ONE

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“…In contrast, the correlation with EF (Figure S3) is stronger than inferred from reconstructions and does not show a clear transition between soil moisture versus energy‐limited evapotranspiration around 55°N, as suggested by the ERAI‐Land and MTE data sets. While this discrepancy might be the evidence of a too strong land‐atmosphere coupling (e.g., Levine et al, ; Vilesa et al, ), it might be also due to inconsistencies between the reconstructed soil moisture and EF, the latter being based on an empirical upscaling technique and a very limited number of in situ observations. As a result, and owing to the limited length of this data set, we will not use the MTE reconstruction to constrain the CMIP5 projections in the continuation of the study.…”

Section: Experiments Design and Data Setsmentioning

confidence: 99%

Midlatitude Summer Drying: An Underestimated Threat in CMIP5 Models?

Douville

Plazzotta

2017

Geophysical Research Letters

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Early assessments of the hydrological impacts of global warming suggested both an intensification of the global water cycle and an expansion of dry areas. Yet these alarming conclusions were challenged by a number of latter studies emphasizing the lack of evidence in observations and historical simulations, as well as the large uncertainties in climate projections from the fifth phase of the Coupled Model Intercomparison Project (CMIP5). Here several aridity indices and a two‐tier attribution strategy are used to demonstrate that a summer midlatitude drying has recently emerged over the northern continents, which is mainly attributable to anthropogenic climate change. This emerging signal is shown to be the harbinger of a long‐term drying in the CMIP5 projections. Linear trends in the observed aridity indices can therefore be used as observational constraints and suggest that the projected midlatitude summer drying was underestimated by most CMIP5 models. Mitigating global warming therefore remains a priority to avoid dangerous impacts on global water and food security.

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“…The accurate simulation of surface fluxes, such as evapotranspiration (ET), is essential to reduce the uncertainties in predictions of the long-term evolution of the terrestrial water cycle and the occurrence of hydro-meteorological hazards (Seneviratne et al, 2010; Vilasa et al, 2017). The different ways in which the control of soil moisture on ET dynamics is modelled has been identified as a major source of discrepancies among land surface model (LSM) predictions (Seneviratne et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Capability of the variogram to quantify the spatial patterns of surface fluxes and soil moisture simulated by land surface models

Garrigues

Verhoef

Blyth

et al. 2021

Progress in Physical Geography: Earth and Environment

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Up to now, relatively little effort has been dedicated to the quantitative assessment of the differences in spatial patterns of model outputs. In this paper, we employed a variogram-based methodology to quantify the differences in the spatial patterns of root-zone soil moisture, net radiation, and latent and sensible heat fluxes simulated by three land surface models (SURFEX/ISBA, JULES and CHTESSEL) over three European geographic domains – namely, UK, France and Spain. The model output spatial patterns were quantified through two metrics derived from the variogram: i) the variogram sill, which quantifies the degree of spatial variability of the data; and ii) the variogram integral range, which represents the spatial length scale of the data. The higher seasonal variation of the spatial variability of sensible and latent heat fluxes over France and Spain, compared to the UK, is related to a more frequent occurrence of a soil-moisture-limited evapotranspiration regime during summer dry spells in the south of France and Spain. The small differences in spatial variability of net radiation between models indicate that the spatial patterns of net radiation are mostly driven by the climate forcing data set. However, the models exhibit larger differences in latent and sensible heat flux spatial variabilities, which are related to their differences in i) soil and vegetation ancillary datasets and ii) physical process representation. The highest discrepancies in spatial patterns between models are observed for soil moisture, which is mainly related to the type of soil hydraulic function implemented in the models. This work demonstrates the capability of the variogram to enhance our understanding of the spatiotemporal structure of the uncertainties in land surface model outputs. Therefore, we strongly encourage the implementation of the variogram metrics in model intercomparison exercises.

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Global soil moisture bimodality in satellite observations and climate models

Cited by 21 publications

References 85 publications

Downscaling satellite soil moisture using geomorphometry and machine learning

Downscaling satellite soil moisture using geomorphometry and machine learning

Midlatitude Summer Drying: An Underestimated Threat in CMIP5 Models?

Capability of the variogram to quantify the spatial patterns of surface fluxes and soil moisture simulated by land surface models

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