Similarity Assessment of Land Surface Model Outputs in the North American Land Data Assimilation System

Kumar, Sujay V.; Wang, Shugong; Mocko, David M.; Peters‐Lidard, Christa D.; Xia, Youlong

doi:10.1002/2017wr020635

Cited by 36 publications

(30 citation statements)

References 54 publications

(70 reference statements)

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“…Multiple land surface models are used in Land Data Assimilation Systems (LDAS) such as Global LDAS (GLDAS), North American LDAS (NLDAS), and FEWS NET LDAS (FLDAS) to increase simulation skill along with the use of hybrid forcing ensembles [57,58]. Also, a multi-model ensemble framework is used to develop estimates of model uncertainty and understand the level of similarity and dissimilarity between the constituent models.…”

Section: Discussionmentioning

confidence: 99%

Uncertainties in Evapotranspiration Estimates over West Africa

et al. 2019

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An evapotranspiration (ET) ensemble composed of 36 land surface model (LSM) experiments and four diagnostic datasets (GLEAM, ALEXI, MOD16, and FLUXNET) is used to investigate uncertainties in ET estimate over five climate regions in West Africa. Diagnostic ET datasets show lower uncertainty estimates and smaller seasonal variations than the LSM-based ET values, particularly in the humid climate regions. Overall, the impact of the choice of LSMs and meteorological forcing datasets on the modeled ET rates increases from north to south. The LSM formulations and parameters have the largest impact on ET in humid regions, contributing to 90% of the ET uncertainty estimates. Precipitation contributes to the ET uncertainty primarily in arid regions. The LSM-based ET estimates are sensitive to the uncertainty of net radiation in arid region and precipitation in humid region. This study serves as support for better determining water availability for agriculture and livelihoods in Africa with earth observations and land surface models.

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Section: Discussionmentioning

confidence: 99%

Uncertainties in Evapotranspiration Estimates over West Africa

et al. 2019

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“…The three LSMs represent a mix of models with significant differences in parameterizations and model physics, as documented in Kumar et al (2017). The community Noah LSM is the land model currently used by NCEP and the United States Department of Defense to support their operational land analyses.…”

Section: Land Surface Models and Configurationmentioning

confidence: 99%

Evaluating the Uncertainty of Terrestrial Water Budget Components Over High Mountain Asia

et al. 2019

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This study explores the uncertainties in terrestrial water budget estimation over High Mountain Asia (HMA) using a suite of uncoupled land surface model (LSM) simulations. The uncertainty in the water balance components of precipitation (P), evapotranspiration (ET), runoff (R), and terrestrial water storage (TWS) is significantly impacted by the uncertainty in the driving meteorology, with precipitation being the most important boundary condition. Ten gridded precipitation datasets along with a mix of model-, satellite-, and gauge-based products, are evaluated first to assess their suitability for LSM simulations over HMA. The datasets are evaluated by quantifying the systematic and random errors of these products as well as the temporal consistency of their trends. Though the broader spatial patterns of precipitation are generally well captured by the datasets, they differ significantly in their means and trends. In general, precipitation datasets that incorporate information from gauges are found to have higher accuracy with low Root Mean Square Errors and high correlation coefficient values. An ensemble of LSM simulations with selected subset of precipitation products is then used to produce the mean annual fluxes and their uncertainty over HMA in P, ET, and R to be 2.11 ± 0.45, 1.26 ± 0.11, and 0.85 ± 0.36 mm per day, respectively. The mean annual estimates of the surface mass (water) balance components from this model ensemble are comparable to global estimates from prior studies. However, the uncertainty/spread of P, ET, and R is significantly larger than the corresponding estimates from global Yoon et al.HMA Water Budget Evaluation studies. A comparison of ET, snow cover fraction, and changes in TWS estimates against remote sensing-based references confirms the significant role of the input meteorology in influencing the water budget characterization over HMA and points to the need for improving meteorological inputs.

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“…In both CLSM and Mosaic, the transpiration formulations follow the model of [39]. More detailed description of the model physics formulations of these models is provided in [27].…”

Section: Modelsmentioning

confidence: 99%

“…NLDAS is a multi-institution effort, which runs four LSMs operationally (Noah version 2.8, [20][21][22]; Mosaic, [23,24]; Variable Infiltration Capacity (VIC) version 4.0.3, [25]; and Sacramento Soil Moisture Accounting (SAC), [26]) using observation-based meteorological data. More recent efforts have focused on upgrading the current suite of NLDAS models ( [27]). We use the outputs from both the operational NLDAS Phase 2 (NLDAS-2) and from four additional models planned for the next phase of NLDAS (Noah version 3.6, [28,29]; Catchment LSM, [30,31]; VIC version 4.1.2l, [32]; and a configuration of Noah-MP, [33,34]) in the NLDAS-2 configuration.…”

Section: Introductionmentioning

confidence: 99%

Attribution of Flux Partitioning Variations between Land Surface Models over the Continental U.S.

et al. 2018

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Accurate quantification of the terrestrial evapotranspiration (ET) components of plant transpiration (T), soil evaporation (E) and evaporation of the intercepted water (I) is necessary for improving our understanding of the links between the carbon and water cycles. Recent studies have noted that, among the modeled estimates, large disagreements exist in the relative contributions of T, E and I to the total ET. As these models are often used in data assimilation environments for incorporating and extending ET relevant remote sensing measurements, understanding the sources of inter-model differences in ET components is also necessary for improving the utilization of such remote sensing measurements. This study quantifies the contributions of two key factors explaining inter-model disagreements to the uncertainty in total ET: (1) contribution of the local partitioning and (2) regional distribution of ET. The analysis is conducted by using outputs from a suite of land surface models in the North American Land Data Assimilation System (NLDAS) configuration. For most of these models, transpiration is the dominant component of the ET partition. The results indicate that the uncertainty in local partitioning dominates the inter-model spread in modeled soil evaporation E. The inter-model differences in T are dominated by the uncertainty in the distribution of ET over the Eastern U.S. and the local partitioning uncertainty in the Western U.S. The results also indicate that uncertainty in the T estimates is the primary driver of total ET errors. Over the majority of the U.S., the contribution of the two factors of uncertainty to the overall uncertainty is non-trivial.

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Similarity Assessment of Land Surface Model Outputs in the North American Land Data Assimilation System

Cited by 36 publications

References 54 publications

Uncertainties in Evapotranspiration Estimates over West Africa

Uncertainties in Evapotranspiration Estimates over West Africa

Evaluating the Uncertainty of Terrestrial Water Budget Components Over High Mountain Asia

Attribution of Flux Partitioning Variations between Land Surface Models over the Continental U.S.

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