High-performance Earth system modeling with NASA/GSFC’s Land Information System

Peters‐Lidard, Christa D.; Houser, Paul R.; Tian, Yudong; Kumar, Sujay V.; Geiger, J.; Olden, S.; Lighty, L.; Doty, B.; Dirmeyer, Paul A.; Adams, Jennifer; Mitchell, K. J.; Wood, Eric F.; Sheffield, Justin

doi:10.1007/s11334-007-0028-x

Cited by 205 publications

(125 citation statements)

References 33 publications

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“…The algorithm scenarios were run within the Land Information System (LIS) platform, a NASA Goddard Space Flight Center software framework with high performance land surface modeling and data assimilation capabilities (Kumar et al 2006;Peters-Lidard et al 2007). Both the global and regional 1-day intensity-duration thresholds were tested for each susceptibility map.…”

Section: Regional Approach Versus Global Algorithm Evaluationmentioning

confidence: 99%

Advances in landslide nowcasting: evaluation of a global and regional modeling approach

et al. 2011

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The increasing availability of remotely sensed data offers a new opportunity to address landslide hazard assessment at larger spatial scales. A prototype global satellite-based landslide hazard algorithm has been developed to identify areas that may experience landslide activity. This system combines a calculation of static landslide susceptibility with satellite-derived rainfall estimates and uses a threshold approach to generate a set of 'nowcasts' that classify potentially hazardous areas. A recent evaluation of this algorithm framework found that while this tool represents an important first step in larger-scale near real-time landslide hazard assessment efforts, it requires several modifications before it can be fully realized as an operational tool. This study draws upon a prior work's recommendations to develop a new approach for considering landslide susceptibility and hazard at the regional scale. This case study calculates a regional susceptibility map using remotely sensed and in situ information and a database of landslides triggered by Hurricane Mitch in 1998 over four countries in Central America. The susceptibility map is evaluated with a regional rainfall intensity-duration triggering threshold and results are compared with the global algorithm framework for the same event. Evaluation of this regional system suggests that this empirically based approach provides one plausible way to approach some of the data and resolution issues identified in the global assessment. The presented methodology is straightforward to implement, improves upon the global approach, and allows for results to be transferable between regions. The results also highlight several remaining challenges, including the empirical nature of the algorithm framework and adequate information for algorithm validation. Conclusions suggest that integrating additional triggering factors such as soil moisture may help to improve algorithm performance accuracy. The regional algorithm scenario represents an important step forward in advancing regional and global-scale landslide hazard assessment.

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Section: Regional Approach Versus Global Algorithm Evaluationmentioning

confidence: 99%

Advances in landslide nowcasting: evaluation of a global and regional modeling approach

et al. 2011

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“…It is also used in land-data assimilation systems such as the North America Land Data Assimilation System , the Land Information System (Peters-Lidard et al, 2007), and HRLDAS (Chen et al, 2007). The model has been advanced numerous times to accurately predict warm-and cold-season processes, which resulted in various versions of the Noah LSM.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Utah Energy Balance (UEB) snow model in the Noah land-surface model

Sultana

Hsu

et al. 2014

Hydrol. Earth Syst. Sci.

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Abstract. Noah (version 2.7.1), the community landsurface model (LSM) of National Centers for Environmental Predictions-National Center for Atmospheric Research (NCEP-NCAR), which is widely used to describe the landsurface processes either in stand-alone or in coupled landatmospheric model systems, is recognized to underestimate snow-water equivalent (SWE). Noah's SWE bias can be attributed to its simple snow sub-model, which does not effectively describe the physical processes during snow accumulation and melt period. To improve SWE simulation in the Noah LSM, the Utah Energy Balance (UEB) snow model is implemented in Noah to test alternate snow surface temperature and snowmelt outflow schemes. Snow surface temperature was estimated using the force-restore method and snowmelt event is regulated by accounting for the internal energy of the snowpack. The modified Noah's SWE simulations are compared with the SWE observed at California's NRCS SNOTEL stations for 7 water years: 2002-2008, while the model's snow surface temperature is verified with observed surface-temperature data at an observation site in Utah. The experiments show that modification in Noah's snow process substantially reduced SWE estimation bias while keeping the simplicity of the Noah LSM. The results suggest that the model did not benefit from the alternate temperature representation but primary improvement can be attributed to the substituted snowmelt process.

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“…It executes at 1/8th-degree grid spacing at an hourly temporal scale over central North America, enabled by the Land Information System (LIS; Kumar et al, 2006;Peters-Lidard et al, 2007). LIS is a scalable land data assimilation system that integrates a suite of advanced LSMs, high resolution satellite and observational data, data assimilation and parameter optimization techniques, and high-performance computing tools (Kumar et al, 2006;Peters-Lidard et al, 2007). Outputs from three LSMs (Mosaic, Noah, and VIC) over a 30-year historical period were used to answer the study questions posed above.…”

Section: North American Land Data Assimilation System Phase 2 (Nldas-2)mentioning

confidence: 99%

“…This process has a significant influence on the recharge estimates because the recharge in the LSMs is the function of water content in the bottom layer. These differences could be related to energy balance or water balance constraints (Mitchell et al, 2004;Peters-Lidard et al, 2007;Rodell et al, 2011;Xia et al, 2012b;Cai et al, 2014).…”

Section: Comparing Et: Among Models and With Modis Etmentioning

confidence: 99%

Comparing potential recharge estimates from three Land Surface Models across the western US

Niraula

Meixner

Ajami

et al. 2017

Journal of Hydrology

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a b s t r a c tGroundwater is a major source of water in the western US. However, there are limited recharge estimates in this region due to the complexity of recharge processes and the challenge of direct observations. Land surface Models (LSMs) could be a valuable tool for estimating current recharge and projecting changes due to future climate change. In this study, simulations of three LSMs (Noah, Mosaic and VIC) obtained from the North American Land Data Assimilation System (NLDAS-2) are used to estimate potential recharge in the western US. Modeled recharge was compared with published recharge estimates for several aquifers in the region. Annual recharge to precipitation ratios across the study basins varied from 0.01% to 15% for Mosaic, 3.2% to 42% for Noah, and 6.7% to 31.8% for VIC simulations. Mosaic consistently underestimates recharge across all basins. Noah captures recharge reasonably well in wetter basins, but overestimates it in drier basins. VIC slightly overestimates recharge in drier basins and slightly underestimates it for wetter basins. While the average annual recharge values vary among the models, the models were consistent in identifying high and low recharge areas in the region. Models agree in seasonality of recharge occurring dominantly during the spring across the region. Overall, our results highlight that LSMs have the potential to capture the spatial and temporal patterns as well as seasonality of recharge at large scales. Therefore, LSMs (specifically VIC and Noah) can be used as a tool for estimating future recharge in data limited regions.

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High-performance Earth system modeling with NASA/GSFC’s Land Information System

Cited by 205 publications

References 33 publications

Advances in landslide nowcasting: evaluation of a global and regional modeling approach

Advances in landslide nowcasting: evaluation of a global and regional modeling approach

Evaluating the Utah Energy Balance (UEB) snow model in the Noah land-surface model

Comparing potential recharge estimates from three Land Surface Models across the western US

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