ORCID Profile
0000-0002-6834-5844
Current Organisations
The University of Texas at Austin
,
Pacific Northwest National Laboratory
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: American Geophysical Union (AGU)
Date: 04-2021
DOI: 10.1029/2020MS002214
Publisher: American Geophysical Union (AGU)
Date: 07-2021
DOI: 10.1029/2020WR028846
Abstract: Multi‐physics ensembles have emerged as a promising approach to hydrological simulations. As multi‐physics ensembles are constructed by perturbing the model physics, the ensemble members share a substantial portion of the same physics and hence are not independent of each other. It is unknown whether and to what extent this nonindependence affects the skill gain of the ensemble method, especially compared with the multi‐model ensemble approach. This study compares a multi‐physics ensemble configured from the Noah land surface model with multi‐parameterization options (Noah‐MP) with the North American Land Data Assimilation System (NLDAS) multi‐model ensemble. The two ensembles are evaluated in terms of the annual cycle and interannual anomaly at 12 River Forecast Centers over the conterminous United States. The ensemble skill gain is measured by the difference between the performance of the ensemble mean and the average of the ensemble members' performance, and the inter‐member independence is measured by error correlations. Results show that, due to the improved model physics, the Noah‐MP configurations outperform, on average, the NLDAS models, especially in the snow‐dominated areas. The Noah‐MP ensemble almost always obtains an outstanding member that performs the best among the two ensembles, reflecting its dense s ling of the feasible model physics space. However, these two performance superiorities do not lead to a superiority of the ensemble mean. The Noah‐MP ensemble has a lower ensemble skill gain, which corresponds to the lower inter‐member independence. These results highlight the importance of inter‐member independence, particularly when most hydrological ensemble methods have overlooked it.
Publisher: American Geophysical Union (AGU)
Date: 12-2020
DOI: 10.1029/2020MS002132
Abstract: We develop a novel framework for rigorously evaluating land surface models (LSMs) against observations by recognizing the asymmetry between verification‐ and falsification‐oriented approaches. The former approach cannot completely verify LSMs even though it exhausts every case of consistency between the model predictions and observations, whereas the latter only requires a single case of inconsistency to reveal that there must be something wrong. We argue that it is such an inconsistency that stimulates further development of the models and enhancement of the observations. We therefore propose a falsification‐oriented signature‐based evaluation framework to identify cases of inconsistency between model predictions and observations by extracting signatures based on a set of key assumptions. We apply this framework to evaluate an ensemble of simulations from the Noah‐MP LSM against observations over the continental United States under the three assumptions of water mass conservation, no lateral water flow, and a sufficiently long period of time. Regions showing inconsistencies between the Noah‐MP ensemble simulations and the observations are located in the western mountainous areas, the Yellowstone river basin, the lower Floridan aquifer, the Niobrara river basin at the north tip of the Ogallala aquifer, and the basins downstream of the Balcones fault zones in Texas. These regions coincide with the sites where both advances in theoretical modeling and new observational data (e.g., from the Critical Zone Observatories) have emerged.
Publisher: Copernicus GmbH
Date: 06-10-2022
Abstract: Abstract. Terrestrial water budget (TWB) data over large domains are of high interest for various hydrological applications. Spatiotemporally continuous and physically consistent estimations of TWB rely on land surface models (LSMs). As an augmentation of the operational North American Land Data Assimilation System Phase 2 (NLDAS‑2) four-LSM ensemble, this study presents a 48-member perturbed-physics ensemble configured from the Noah LSM with multi-physics options (Noah‑MP). The 48 Noah‑MP physics configurations are selected to give a representative cross-section of commonly used LSMs for parameterizing runoff, atmospheric surface layer turbulence, soil moisture limitation on photosynthesis, and stomatal conductance. The ensemble simulated the 1980–2015 monthly TWB over the conterminous United States (CONUS) at a 1/8° spatial resolution. Simulation outputs include total evapotranspiration and its constituents (canopy evaporation, soil evaporation, and transpiration), runoff (the surface and subsurface components), as well as terrestrial water storage (snow water equivalent, four-layer soil water content from the surface down to 2 m, and the groundwater storage anomaly). This dataset is available at 0.5281/zenodo.7109816 (Zheng et al., 2022). Evaluations carried out in this study and previous investigations show that the ensemble performs well in reproducing the observed terrestrial water storage, snow water equivalent, soil moisture, and runoff. Noah-MP complements the NLDAS models well, and adding Noah-MP consistently improves the NLDAS estimations of the above variables in most areas of CONUS. Besides, the perturbed-physics ensemble facilities the identification of model deficiencies. The parameterizations of shallow snow, lakes, and near-surface atmospheric turbulence should be improved in future model versions.
Publisher: American Geophysical Union (AGU)
Date: 2019
DOI: 10.1029/2017WR022236
Abstract: The precipitation partitioning between evapotranspiration (ET) and runoff (R) at the land surface is controlled by atmospheric boundary layer and terrestrial hydrological processes. These processes in land surface models are manifested primarily as stomatal conductance, soil moisture limitation factor to transpiration (β‐factor), turbulence, and runoff generation. What are the sensitivities of precipitation partitioning to the parameterizations of these processes? To address this overarching question, the annual and seasonal means of ET and R over the conterminous United States were simulated using 48 configurations of the Noah land surface model with multiparameterization options (Noah‐MP). The Sobol' total sensitive index was used to quantify the sensitivity of ET and R to the parameterizations of the four processes mentioned above. Results show that the sensitivities of the annual means depend on climatic conditions and the interplay between ET and R plays an important role. In humid regions, precipitation is mostly partitioned into R, whereas the simulations can be more sensitive to ET's parameterizations. In arid regions, ET accounts for the major partition, whereas the simulations can be more sensitive to the runoff parameterization. Seasonal means exhibit different sensitivities from the annual means. The seasonal mean ET is more sensitive to ET's parameterizations, and R is more sensitive to the runoff parameterization. The β‐factor, which is neglectable for the annual means, is important for summer‐time ET. Mediated by the terrestrial water storage memories, ET interplays R across seasons. The winter‐time R is still sensitive to the stomatal conductance that only modulates growing‐season ET.
Publisher: Copernicus GmbH
Date: 04-07-2023
DOI: 10.5194/ESSD-15-2755-2023
Abstract: Abstract. Terrestrial water budget (TWB) data over large domains are of high interest for various hydrological applications. Spatiotemporally continuous and physically consistent estimations of TWB rely on land surface models (LSMs). As an augmentation of the operational North American Land Data Assimilation System Phase 2 (NLDAS-2) four-LSM ensemble, this paper describes a dataset simulated from an ensemble of 48 physics configurations of the Noah LSM with multi-physics options (Noah-MP). The 48 Noah-MP physics configurations are selected to give a representative cross-section of commonly used LSMs for parameterizing runoff, atmospheric surface layer turbulence, soil moisture limitation on photosynthesis, and stomatal conductance. The dataset spans from 1980 to 2015 over the conterminous United States (CONUS) at a monthly temporal resolution and a 1/8∘ spatial resolution. The dataset variables include total evapotranspiration and its constituents (canopy evaporation, soil evaporation, and transpiration), runoff (the surface and subsurface components), as well as terrestrial water storage (snow water equivalent, four-layer soil water content from the surface down to 2 m, and the groundwater storage anomaly). The dataset is available at 0.5281/zenodo.7109816 (Zheng et al., 2022). Evaluations carried out in this study and previous investigations show that the ensemble performs well in reproducing the observed terrestrial water storage, snow water equivalent, soil moisture, and runoff. Noah-MP complements the NLDAS models well, and adding Noah-MP consistently improves the NLDAS estimations of the above variables in most areas of CONUS. Besides, the perturbed-physics ensemble facilitates the identification of model deficiencies. The parameterizations of shallow snow, spatially varying groundwater dynamics, and near-surface atmospheric turbulence should be improved in future model versions.
Publisher: Copernicus GmbH
Date: 06-10-2022
Location: United States of America
No related grants have been discovered for Lingcheng Li.