Evaluation of multi-model simulated soil moisture in NLDAS-2

Xia, Youlong; Sheffield, Justin; Ek, Michael; Dong, Jing; Chaney, Nathaniel W.; Wei, Helin; Meng, Jesse; Wood, Eric F.

doi:10.1016/j.jhydrol.2014.02.027

Cited by 181 publications

(168 citation statements)

References 59 publications

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“…High correlations have been observed between RS-derived drought indices and soil moisture at different depths [36]. The correlation between soil moisture based on ground measurements and RS-derived drought indices was used to evaluate drought severity and validate the accuracy of the RS-derived drought indices.…”

Section: Station Levelmentioning

confidence: 99%

Suitability Assessment of Satellite-Derived Drought Indices for Mongolian Grassland

Chang

Niu

et al. 2017

Remote Sensing

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Abstract:In Mongolia, drought is a major natural disaster that can influence and devastate large regions, reduce livestock production, cause economic damage, and accelerate desertification in association with destructive human activities. The objective of this article is to determine the optimal satellite-derived drought indices for accurate and real-time expression of grassland drought in Mongolia. Firstly, an adaptability analysis was performed by comparing nine remote sensing-derived drought indices with reference indicators obtained from field observations using several methods (correlation, consistency percentage (CP), and time-space analysis). The reference information included environmental data, vegetation growth status, and region drought-affected (RDA) information at diverse scales (pixel, county, and region) for three types of land cover (forest steppe, steppe, and desert steppe). Second, a meteorological index (PED), a normalized biomass (NorBio) reference indicator, and the RDA-based drought CP method were adopted for describing Mongolian drought. Our results show that in forest steppe regions the normalized difference water index (NDWI) is most sensitive to NorBio (maximum correlation coefficient (MAX_R): up to 0.92) and RDA (maximum CP is 87%), and is most consistent with RDA spatial distribution. The vegetation health index (VHI) and temperature condition index (TCI) are most correlated with the PED index (MAX_R: 0.75) and soil moisture (MAX_R: 0.58), respectively. In steppe regions, the NDWI is most closely related to soil moisture (MAX_R: 0.69) and the VHI is most related to the PED (MAX_R: 0.76), NorBio (MCC: 0.95), and RDA data (maximum CP is 89%), exhibiting the most consistency with RDA spatial distribution. In desert steppe areas, the vegetation condition index (VCI) correlates best with NorBio (MAX_R: 0.92), soil moisture (MAX_R: 0.61), and RDA spatial distribution, while TCI correlates best with the PED (MAX_R: 0.75) and the RDA data (maximum CP is 79%). The VHI is a combination of constructed VCI and TCI, and can be used instead of them. Finally, the mode method was adopted to identify appropriate drought indices. The best two indices (VHI and NDWI) can be utilized to develop a combination drought model for accurately monitoring and quantifying drought in the future. Additionally, the new framework can be adopted to investigate and analyze the suitability of satellite-derived drought indices and determine the most appropriate index/indices for other countries or areas.

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Section: Station Levelmentioning

confidence: 99%

Suitability Assessment of Satellite-Derived Drought Indices for Mongolian Grassland

Chang

Niu

et al. 2017

Remote Sensing

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“…Many other off-line land surface data assimilation systems also don't currently assimilate satellite derived surface soil wetness or satellite derived LST. Examples of such off-line systems are; i) the North American Land Data Assimilation System (NLDAS; Xia et al 2014), ii) the ERA-Interim/Land reanalysis (Balsamo et al 2015) and iii) the Modern-Era Retrospective analysis for Research and Applications (MERRA) land system (Reichle et al 2011). Recently, the JULES land surface model has been incorporated into the NASA Land Information System (LIS; Kumar et al 2008) and it is anticipated that a future JASMIN system will use LIS for data assimilation to assimilate remotely sensed measurements of surface soil wetness and land surface temperature.…”

Section: Discussionmentioning

confidence: 99%

JASMIN: A prototype high resolution soil moisture analysis system for Australia

Dharssi¹,

Kumar²

2017

Bureau Research Report

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“…Therefore, the Global Climate Observing System initiative (2010) has identified soil moisture as an essential climate variable. However, soil moisture products from state-of-the-art land surface models (LSMs) show large biases compared to in situ observations (Xia et al, 2014;Zheng et al, 2015) and large variation among different models (Dirmeyer et al, 2006;Xia et al, 2014). Xia et al (2014) pointed out that, in particular, the soil moisture outcomes from LSMs need improvement.…”

Section: Introductionmentioning

confidence: 99%

“…Estimations of vegetation characteristics by microwave and optical sensors also have the potential to provide estimates of root zone soil moisture (Van Emmerik et al, 2015;Petropoulos et al, 2015;Steele-Dunne et al, 2012;Wang et al, 2010). Regarding land process models, the implemented model physics, model structure, the quality of parameterizations, and the imposed initial and boundary conditions (including atmospheric forcing terms) determine the reliability of model results (Xia et al, 2014). Combining observations of earth variables with process models by data assimilation techniques is interesting in estimating initial model states, model state updating and parameter calibration, thereby improving the model accuracy (Houser et al, 2012;Reichle, 2008;Vereecken et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

The Raam regional soil moisture monitoring network in the Netherlands

Benninga

Carranza

Pezij

et al. 2018

Earth Syst. Sci. Data

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Abstract.We have established a soil moisture profile monitoring network in the Raam region in the Netherlands. This region faces water shortages during summers and excess of water during winters and after extreme precipitation events. Water management can benefit from reliable information on the soil water availability and water storing capacity in the unsaturated zone. In situ measurements provide a direct source of information on which water managers can base their decisions. Moreover, these measurements are commonly used as a reference for the calibration and validation of soil moisture content products derived from earth observations or obtained by model simulations. Distributed over the Raam region, we have equipped 14 agricultural fields and 1 natural grass field with soil moisture and soil temperature monitoring instrumentation, consisting of Decagon 5TM sensors installed at depths of 5, 10, 20, 40 and 80 cm. In total, 12 stations are located within the Raam catchment (catchment area of 223 km 2 ), and 5 of these stations are located within the closed sub-catchment Hooge Raam (catchment area of 41 km 2 ). Soil-specific calibration functions that have been developed for the 5TM sensors under laboratory conditions lead to an accuracy of 0.02 m 3 m −3 . The first set of measurements has been retrieved for the period 5 April 2016-4 April 2017. In this paper, we describe the Raam monitoring network and instrumentation, the soil-specific calibration of the sensors, the first year of measurements, and additional measurements (soil temperature, phreatic groundwater levels and meteorological data) and information (elevation, soil physical characteristics, land cover and a geohydrological model) available for performing scientific research. The data are available at https://doi.org/10.4121/uuid:dc364e97-d44a-403f-82a7-121902deeb56.

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Evaluation of multi-model simulated soil moisture in NLDAS-2

Cited by 181 publications

References 59 publications

Suitability Assessment of Satellite-Derived Drought Indices for Mongolian Grassland

Suitability Assessment of Satellite-Derived Drought Indices for Mongolian Grassland

JASMIN: A prototype high resolution soil moisture analysis system for Australia

The Raam regional soil moisture monitoring network in the Netherlands

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