Inter-comparison of satellite-retrieved and Global Land Data Assimilation System-simulated soil moisture datasets for global drought analysis

Liu, Yuanbo; Wang, Wen

doi:10.1016/j.rse.2018.10.026

Cited by 101 publications

(47 citation statements)

References 69 publications

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“…Earth System Science Data to measure extremely dry or extremely wet conditions (McColl et al, 2017;Liu et al, 2019); consequently, this lack of information influences the prediction capacity of our downscaling framework and there is a need to improve modeling and measurements of these extremes. In addition, the quality of the prediction factors will impact the quality of final prediction outcomes.…”

Section: Open Accessmentioning

confidence: 99%

“…This scale mismatch has been previously identified when testing different soil moisture patterns (Nicolai-Shaw et al, 2015) as field soil moisture records are usually representative of <1 m 3 of soil while satellite and modeling estimates varies from several meters to multiple kilometers. Soil moisture measurements (from satellites and in situ measurements) across both water-limited environment and tropical areas are extremely limited (Liu et al, 2019), a condition that increases prediction variances (and consequently https://doi.org/10.5194/essd-2020-264 also increased model uncertainty). Thus, alternative modeling and evaluation frameworks and model evaluation statistics are required to provide more information to better interpret the spatial variability and dynamics of soil moisture global estimates (Gruber et al, 2020).…”

Section: Open Accessmentioning

confidence: 99%

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Gap-Free Global Annual Soil Moisture: 15 km Grids for 1991–2018

Guevara¹,

Taufer²,

Vargas³

2020

Preprint

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Abstract. Soil moisture is key for quantifying soil-atmosphere interactions. We provide a soil moisture pattern recognition framework to increase the spatial resolution and fill gaps of the ESA-CCI (European Space Agency-Climate Change Initiative v4.5) soil moisture dataset, which contains more than 40 years of satellite soil moisture global grids with a spatial resolution of ~27 km. We use terrain parameters coupled with bioclimatic and soil type information to predict the finer-grained satellite soil moisture. We assess the impact of terrain parameters on the prediction accuracy by cross-validating the pattern recognition of soil moisture with and without the support of bioclimatic and soil type information. The outcome is a new dataset of gap-free global mean annual soil moisture and uncertainty for 28 years (1991–2018) across 15 km grids. We use independent in situ records from the International Soil Moisture Network (ISMN, n = 13376) and in situ precipitation records (n = 4909) only for evaluating the new dataset. Cross-validated correlation between observed and predicted soil moisture values varies from r = 0.69 to r = 0.87 with root mean squared errors (RMSE, m3/m3) around 0.03 and 0.04. Our soil moisture predictions improve: (a) the correlation with the ISMN (when compared with the original ESA-CCI dataset) from r = 0.30 (RMSE = 0.09, ubRMSE = 0.37) to r = 0.66 (RMSE = 0.05, ubRMSE = 0.18); and (b) the correlation with local precipitation records across boreal (from r =

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Section: Open Accessmentioning

confidence: 99%

Section: Open Accessmentioning

confidence: 99%

Gap-Free Global Annual Soil Moisture: 15 km Grids for 1991–2018

Guevara¹,

Taufer²,

Vargas³

2020

Preprint

View full text Add to dashboard Cite

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“…4). This subestimation, as explained in previous work (McColl et al, 2017, Liu et al, 2019, is because satellite soil moisture sensors are not able to provide accurate estimates across extremely dry conditions or across areas where water aboveground is higher than water belowground (e.g., extremely humid conditions). For a better calibration and understanding the main limitations of satellite soil moisture measurements across multiple environmental conditions, there is an increasing number of studies reporting validation performances across multiple scales (spatial and temporal) of available soil moisture satellite datasets (An et al, 2016;Colliander et al, 2017b;Dorigo et al, 2011b;Minet et al, 2012;Mohanty et al, 2017;Yee et al, 2016).…”

Section: Discussionmentioning

confidence: 98%

“…1). Water limited environments across arid and semi-arid regions where drying trends are prevalent, large discrepancy has been found between satellite and model-based soil moisture estimates (Liu et al, 2019). The lack of field soil moisture information across these areas is a major limitation for interpreting the discrepancies between satellite and model-based soil moisture estimates.…”

Section: Discussionmentioning

confidence: 99%

“…However higher resolution of soil moisture products are needed to precisely quantify the contribution of soil moisture on larnd-surface models (Singh et al, 2015). Although modelbased estimates of soil moisture associated to global land data assimilation systems are available for studying historical soil moisture trends at the global scale (Fang et al, 2009, Liu et al, 2019, they remain represented with spatial resolutions > 15km grids. By increasing the spatial resolution of the ESA-CCI soil moisture product by nearly 50%, we demonstrate the potential of digital terrain analysis to predict satellite soil moisture spatial patterns and trends while increasing the agreement between satellite and field soil moisture records predicting historical soil moisture trends (Fig 6).…”

Section: Discussionmentioning

confidence: 99%

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Gap-Free Global Annual Soil Moisture: 15km Grids for 1991–2016

Guevara

Taufer

Vargas

2019

Preprint

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Abstract. Soil moisture is key for quantifying soil-atmosphere interactions and the ESA-CCI (European Space Agency Climate Change Initiative) provides historical (> 30 years) satellite soil moisture gridded data at the global scale. We evaluate an alternative approach to increase the spatial resolution of the original ESA-CCI soil moisture measurements from 27 km to 15 km grids by coupling machine learning (ML) algorithms with information from digital terrain analysis at the global scale. We modeled mean annual ESA-CCI soil moisture values across 26 years of available data (1991–2016) using a ML based kernel method and multiple terrain parameters (e.g., slope, wetness index) as prediction factors. We used ground information from the International Soil Moisture Network (ISMN, n = 13376) for evaluating soil moisture predictions. We provide gap-free mean annual soil moisture predictions, which increase by nearly 50 % the spatial resolution of ESA-CCI soil moisture product. Our predictions showed a statistical accuracy varying 0.69–0.87 % and 0.04 m3/m3 of cross-validated explained variance and root mean squared error (RMSE). We found no significant differences between the ESA-CCI and our predictions, but we found discrepancy between multiple evaluation metrics (e.g., bias vs efficiency) comparing the ESA-CCI with the ISMN. We found a negative bias (−0.01 to −0.08 m3/m3) between the values of ISMN when comparing with the ESA-CCI and our predictions across the analyzed years. A temporal analysis, using a robust trend detection strategy (i.e., Theil-Sen estimator), suggests a decline of soil moisture at the global scale that is consistent in both gridded datasets and field measurements of soil moisture varying from −0.7[−0.77, −0.62] % in the ESA-CCI product, −0.9[−1.01, −0.8] % in the downscaled predictions, and −1.6 [−1.7, −1.5] % in the ISMN. The soil moisture predictions provided here (http://www.hydroshare.org/resource/b940b704429244a99f902ff7cb30a31f) could be useful for quantifying soil moisture spatial and temporal dynamics across areas with low availability of soil moisture information in the original ESA-CCI database.

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Development and propagation of hydrologic drought from meteorological and agricultural drought in the Mekong River Basin

Palanisamy

Narasimhan

Paul³

et al. 2023

Hydrological Processes

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Mekong River Basin (MRB) experiences extreme droughts and floods frequently, due to the precipitation deficit across the basin. The meteorological droughts will have a profound impact on the distribution of soil moisture and hence agricultural productivity, which will lead to the reduction of surface water resources. It is therefore important to evaluate the deficits in hydrometeorological extremes, that is, precipitation, soil moisture, and streamflow to help decipher how one drought results in the other. In this study, a streamflow deficit index (SDI) has been proposed and applied to understand the changes in hydrology due to the fluctuations in precipitation and soil moisture in the Mekong River Basin (MRB). The study used percent normal (PN), a precipitation deficit index, and soil moisture deficit index (SMDI) to identify the initiation and sustenance of hydrological drought using SDI.The SMDI was obtained from soil moisture simulated using the soil and water assessment tool (SWAT) for the periods of 1980-2008. The study results suggested that the proposed index was able to represent the historical river flow deficit that persisted in the basin in the year 1992. Increasing variation in the streamflow deficit in parts of Thailand, Lao PDR, Vietnam, and Cambodia from the year 1992 was also captured by SDI. The flooding year 2000, which resulted in an economic loss of over 200 million USD, was also effectively captured by the proposed index. The lengthening of the streamflow drought, a metric used to represent drought propagation, was shorter by at least 2 months in forested catchments compared with that of agricultural catchments, which implies that any deficit in precipitation and soil moisture, will have a severe impact on agricultural basins. Similarly, the attenuation of both agricultural and hydrological drought was found to be smaller in forested subbasins than those in agricultural subbasins. The findings of the study will be useful for the timely identification of extreme hydrological events and for planning mitigation measures.

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Inter-comparison of satellite-retrieved and Global Land Data Assimilation System-simulated soil moisture datasets for global drought analysis

Cited by 101 publications

References 69 publications

Gap-Free Global Annual Soil Moisture: 15 km Grids for 1991–2018

Gap-Free Global Annual Soil Moisture: 15 km Grids for 1991–2018

Gap-Free Global Annual Soil Moisture: 15km Grids for 1991–2016

Development and propagation of hydrologic drought from meteorological and agricultural drought in the Mekong River Basin

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