A land surface data assimilation framework using the land information system: Description and applications

Kumar, Sujay V.; Reichle, Rolf H.; Peters‐Lidard, Christa D.; Koster, Randal D.; Zhan, Xiwu; Crow, Wade T.; Eylander, John; Houser, Paul R.

doi:10.1016/j.advwatres.2008.01.013

Cited by 198 publications

(185 citation statements)

References 36 publications

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“…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. LIS also includes the CABLE land surface model making it less difficult to adapt the JASMIN system to use CABLE instead of JULES.…”

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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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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“…Various types of satellites measure soil moisture, but recent developments have focused on active and passive microwave sensors (Wang and Qu, 2009). Much of the current research in this area involves developing algorithms to: correctly retrieve soil moisture from these satellites (e.g., Owe et al, 2008;Li et al, 2010;Jackson et al, 2010Jackson et al, , 2012, blend the data appropriately with other data sources (e.g., Kumar et al, 2008;Liu et al, 2011;Peters-Lidard et al, 2011), and quantify the error in the resulting soil moisture datasets (e.g., Harrison et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Full-featured ground vehicle mobility analysis using different soil moisture sources

Stevens¹,

McKinley²,

Vahedifard³

2017

IJVP

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Abstract:We investigated the influence of using three different soil moisture sources WindSat (a satellite), land information system (LIS) (a computer model), and in situ ground sensors in a full-featured ground vehicle mobility analysis model. Soil moisture, soil type, land use, and slope were used to estimate the mobility in dry and wet scenarios for six vehicles using the NATO Reference Mobility Model (NRMM). The dry scenario showed nearly the same results for all three soil moisture sources. In the wet scenario, modelled travel times based on LIS were about 26% faster compared to those from the in situ moisture. Modelled times based on WindSat averaged 11% slower than those from the in situ moisture. For the test case we found agreement between the results of the current full-feature model against a previously-developed model using only soil moisture time series. This demonstrates that soil moisture can be important even as one terrain factor among many.

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“…In this study, we used the EnKF, which is a relatively simple and flexible technique for assimilating satellite data into land surface models (e.g. Draper et al, 2012;Reichle et al, 2002Reichle et al, , 2008Kumar et al, 2008Kumar et al, , 2009Pipunic et al, 2008;Crow and Wood, 2003; …”

Section: Data Assimilation Frameworkmentioning

confidence: 99%

Improving soil moisture and runoff simulations over Europe using a high-resolution data-assimilation modeling framework

Naz

Kurtz

Montzka

et al. 2018

Preprint

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Abstract. Accurate and reliable hydrologic simulations are important for many applications such as water resources management, future water availability projections and predictions of extreme events. However, the accuracy of water balance estimates is limited by the lack of observations at large scales and the uncertainties of model simulations due to errors in model structure and inputs (e.g. hydrologic parameters and atmospheric forcings). In this study, we assimilated 15 ESA CCI soil moisture (SM) information to improve the estimation of continental-scale soil moisture and runoff. The seasons when cross-validated with independent CCI-SM observations. On average, the mean bias in soil moisture was reduced from 0.1 mm 3 /mm 3 in open-loop simulations to 0.004 mm 3 /mm 3 with SM assimilation. The assimilation experiment also shows overall improvements in runoff, particularly during peak runoff. The results demonstrate the potential of assimilating satellite soil moisture observations to improve high-resolution soil moisture and runoff simulations at the continental scale, which is useful for water resources assessment and monitoring. 25

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A land surface data assimilation framework using the land information system: Description and applications

Cited by 198 publications

References 36 publications

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

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

Full-featured ground vehicle mobility analysis using different soil moisture sources

Improving soil moisture and runoff simulations over Europe using a high-resolution data-assimilation modeling framework

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