Assessment of simulated water balance from Noah, Noah‐MP, CLM, and VIC over CONUS using the NLDAS test bed

Cai, Xitian; Yang, Zong‐Liang; Xia, Youlong; Huang, Maoyi; Wei, Helin; Leung, L. Ruby; Ek, Michael

doi:10.1002/2014jd022113

Cited by 142 publications

(159 citation statements)

References 85 publications

(125 reference statements)

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“…Consistent with prior research that has indicated the importance of atmosphere-surface interactions (Cai et al, 2014) and specifically soil moisture Seneviratne et al, 2010) in surface energy partitioning and thus near-surface T and q regimes and static energy, exclusion of SM from the ANN with three hidden layers (i.e., ANN-HL3) decreases model performance relative to ANN-HL3-SM and increases the RMSE for maximum θ e in 70 % of grid cells. The regions for which this impact is most strongly manifest are close to or within the "warming holes" described above and/or are located downstream of regions of significant county-level irrigation and anthropogenic enhancement of SM DeAngelis et al, 2010) (Fig.…”

Section: Resultssupporting

confidence: 70%

Modeling the contributions of global air temperature, synoptic-scale phenomena and soil moisture to near-surface static energy variability using artificial neural networks

2017

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Abstract. The static energy content of the atmosphere is increasing on a global scale, but exhibits important subglobal and subregional scales of variability and is a useful parameter for integrating the net effect of changes in the partitioning of energy at the surface and for improving understanding of the causes of so-called "warming holes" (i.e., locations with decreasing daily maximum air temperatures (T ) or increasing trends of lower magnitude than the global mean). Further, measures of the static energy content (herein the equivalent potential temperature, θ e ) are more strongly linked to excess human mortality and morbidity than air temperature alone, and have great relevance in understanding causes of past heat-related excess mortality and making projections of possible future events that are likely to be associated with negative human health and economic consequences. New nonlinear statistical models for summertime daily maximum and minimum θ e are developed and used to advance understanding of drivers of historical change and variability over the eastern USA. The predictor variables are an index of the daily global mean temperature, daily indices of the synoptic-scale meteorology derived from T and specific humidity (Q) at 850 and 500 hPa geopotential heights (Z), and spatiotemporally averaged soil moisture (SM). SM is particularly important in determining the magnitude of θ e over regions that have previously been identified as exhibiting warming holes, confirming the key importance of SM in dictating the partitioning of net radiation into sensible and latent heat and dictating trends in near-surface T and θ e . Consistent with our a priori expectations, models built using artificial neural networks (ANNs) out-perform linear models that do not permit interaction of the predictor variables (global T , synoptic-scale meteorological conditions and SM). This is particularly marked in regions with high variability in minimum and maximum θ e , where more complex models built using ANN with multiple hidden layers are better able to capture the day-to-day variability in θ e and the occurrence of extreme maximum θ e . Over the entire domain, the ANN with three hidden layers exhibits high accuracy in predicting maximum θ e > 347 K. The median hit rate for maximum θ e > 347 K is > 0.60, while the median false alarm rate is ≈ 0.08.

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Section: Resultssupporting

confidence: 70%

Modeling the contributions of global air temperature, synoptic-scale phenomena and soil moisture to near-surface static energy variability using artificial neural networks

2017

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“…Furthermore, we compare the performance of the CLM with Noah-MP (Niu et al, 2011), which serves as a participant model in Land Data Assimilation Systems (LDAS, Cai et al, 2014). Single-point Noah-MP simulations are conducted in the same way as CLM simulations to ensure their comparability.…”

Section: Model Simulationsmentioning

confidence: 99%

Pairing FLUXNET sites to validate model representations of land-use/land-cover change

Chen

Dirmeyer

Guo

et al. 2018

Hydrol. Earth Syst. Sci.

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Abstract. Land surface energy and water fluxes play an important role in land-atmosphere interactions, especially for the climatic feedback effects driven by land-use/landcover change (LULCC). These have long been documented in model-based studies, but the performance of land surface models in representing LULCC-induced responses has not been investigated well. In this study, measurements from proximate paired (open versus forest) flux tower sites are used to represent observed deforestation-induced changes in surface fluxes, which are compared with simulations from the Community Land Model (CLM) and the Noah MultiParameterization (Noah-MP) land model. Point-scale simulations suggest the CLM can represent the observed diurnal and seasonal changes in net radiation (R net ) and ground heat flux (G), but difficulties remain in the energy partitioning between latent (LE) and sensible (H ) heat flux. The CLM does not capture the observed decreased daytime LE, and overestimates the increased H during summer. These deficiencies are mainly associated with models' greater biases over forest land-cover types and the parameterization of soil evaporation. Global gridded simulations with the CLM show uncertainties in the estimation of LE and H at the grid level for regional and global simulations. Noah-MP exhibits a similar ability to simulate the surface flux changes, but with larger biases in H , G, and R net change during late winter and early spring, which are related to a deficiency in estimating albedo. Differences in meteorological conditions between paired sites is not a factor in these results. Attention needs to be devoted to improving the representation of surface heat flux processes in land models to increase confidence in LULCC simulations.

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“…Generally, CLM-OL overestimated the SWC values for all sub-20 regions and in all seasons. This overestimation of soil moisture in CLM was also reported by Cai et al 2014 when compared with other land surface models and observations over Continental US. However, using data assimilation, this overestimation was reduced consistently in all sub-regions, as shown with CLM-DA.…”

Section: Resultsmentioning

confidence: 52%

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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Assessment of simulated water balance from Noah, Noah‐MP, CLM, and VIC over CONUS using the NLDAS test bed

Cited by 142 publications

References 85 publications

Modeling the contributions of global air temperature, synoptic-scale phenomena and soil moisture to near-surface static energy variability using artificial neural networks

Modeling the contributions of global air temperature, synoptic-scale phenomena and soil moisture to near-surface static energy variability using artificial neural networks

Pairing FLUXNET sites to validate model representations of land-use/land-cover change

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

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