Articles | Volume 18, issue 21
https://doi.org/10.5194/gmd-18-8439-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-8439-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
12 Nov 2025
Model evaluation paper |
| 12 Nov 2025
| 12 Nov 2025
Benchmarking and evaluating the NASA Land Information System (version 7.5.2) coupled with the refactored Noah-MP land surface model (version 5.0)
NSF National Center for Atmospheric Research (NCAR), Boulder, Colorado, USA
Tzu-Shun Lin
NSF National Center for Atmospheric Research (NCAR), Boulder, Colorado, USA
David M. Mocko
NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
Science Applications International Corporation, Greenbelt, Maryland, USA
Ronnie Abolafia-Rosenzweig
NSF National Center for Atmospheric Research (NCAR), Boulder, Colorado, USA
Jerry W. Wegiel
NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
Science Applications International Corporation, Greenbelt, Maryland, USA
Sujay V. Kumar
NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
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Wildfire smoke contains dark particles called tar balls that absorb sunlight and warm the air, but air quality and climate models usually leave them out. We added tar balls to a model of the southeastern United States and compared it with aircraft and ground measurements. Including them, and accounting for the coatings that form around them, raised the sunlight absorbed by smoke by up to 6 %. Models that omit tar balls may therefore underestimate warming from wildfires.
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This preprint is open for discussion and under review for The Cryosphere (TC).
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Chayan Roychoudhury, Rajesh Kumar, Cenlin He, William Y. Y. Cheng, Kirpa Ram, Naoki Mizukami, and Avelino F. Arellano
Earth Syst. Sci. Data, 18, 5209–5257, https://doi.org/10.5194/essd-18-5209-2026, https://doi.org/10.5194/essd-18-5209-2026, 2026
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Naoki Mizukami, Samar Minallah, Cenlin He, Chayan Roychoudhury, William Y.Y. Cheng, and Rajesh Kumar
EGUsphere, https://doi.org/10.5194/egusphere-2025-4586, https://doi.org/10.5194/egusphere-2025-4586, 2025
Short summary
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We examined how air pollution particles that darken snow affect water resources in High Mountain Asia, a region that supplies rivers vital to millions of populations. Using computer models, we found that these particles cause snow to melt two weeks to a month earlier, shifting when water becomes available. This leads to more runoff at first but less later in the year, slightly reducing annual river flow. The findings highlight the need to link air pollution control with water management.
Chayan Roychoudhury, Cenlin He, Rajesh Kumar, and Avelino F. Arellano Jr.
Earth Syst. Dynam., 16, 1237–1266, https://doi.org/10.5194/esd-16-1237-2025, https://doi.org/10.5194/esd-16-1237-2025, 2025
Short summary
Short summary
We present a novel data-driven approach to understand how pollution and weather processes interact to influence snowmelt in Asian glaciers and how these interactions are represented in three climate models. Our findings show where models need improvement in predicting snowmelt, particularly dust and its transport. This method can support future model development for reliable predictions in climate-vulnerable regions.
Rajesh Kumar, Piyush Bhardwaj, Cenlin He, Jennifer Boehnert, Forrest Lacey, Stefano Alessandrini, Kevin Sampson, Matthew Casali, Scott Swerdlin, Olga Wilhelmi, Gabriele G. Pfister, Benjamin Gaubert, and Helen Worden
Earth Syst. Sci. Data, 17, 1807–1834, https://doi.org/10.5194/essd-17-1807-2025, https://doi.org/10.5194/essd-17-1807-2025, 2025
Short summary
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We have created a 14-year hourly air quality dataset at 12 km resolution by combining satellite observations of atmospheric composition with air quality models over the contiguous United States (CONUS). The dataset has been found to reproduce key observed features of air quality over the CONUS. To enable easy visualization and interpretation of county-level air quality measures and trends by stakeholders, an ArcGIS air quality dashboard has also been developed.
Mohamed Hamitouche, Giorgia Fosser, Alessandro Anav, Cenlin He, and Tzu-Shun Lin
Hydrol. Earth Syst. Sci., 29, 1221–1240, https://doi.org/10.5194/hess-29-1221-2025, https://doi.org/10.5194/hess-29-1221-2025, 2025
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This study evaluates how different methods of simulating runoff impact river flow predictions globally. By comparing seven approaches within the Noah-Multi-parameterisation (Noah-MP) land surface model, we found significant differences in accuracy, with some methods underestimating or overestimating runoff. The results are crucial for improving water resource management and flood prediction. Our work highlights the need for precise modelling to better prepare for climate-related challenges.
Min Huang, Gregory R. Carmichael, Kevin W. Bowman, Isabelle De Smedt, Andreas Colliander, Michael H. Cosh, Sujay V. Kumar, Alex B. Guenther, Scott J. Janz, Ryan M. Stauffer, Anne M. Thompson, Niko M. Fedkin, Robert J. Swap, John D. Bolten, and Alicia T. Joseph
Atmos. Chem. Phys., 25, 1449–1476, https://doi.org/10.5194/acp-25-1449-2025, https://doi.org/10.5194/acp-25-1449-2025, 2025
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Louise Busschaert, Michel Bechtold, Sara Modanesi, Christian Massari, Dirk Raes, Sujay V. Kumar, and Gabrielle J. M. De Lannoy
EGUsphere, https://doi.org/10.2139/ssrn.4974019, https://doi.org/10.2139/ssrn.4974019, 2024
Preprint archived
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This study estimates irrigation in the Po Valley using AquaCrop and Noah-MP models with sprinkler irrigation. Noah-MP shows higher annual rates than AquaCrop due to more water losses. After adjusting, both align with reported irrigation ranges (500–600 mm/yr). Soil moisture estimates from both models match satellite data, though both have limitations in vegetation and evapotranspiration modeling. The study emphasizes the need for observations to improve irrigation estimates.
Justin M. Pflug, Melissa L. Wrzesien, Sujay V. Kumar, Eunsang Cho, Kristi R. Arsenault, Paul R. Houser, and Carrie M. Vuyovich
Hydrol. Earth Syst. Sci., 28, 631–648, https://doi.org/10.5194/hess-28-631-2024, https://doi.org/10.5194/hess-28-631-2024, 2024
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Estimates of 250 m of snow water equivalent in the western USA and Canada are improved by assimilating observations representative of a snow-focused satellite mission with a land surface model. Here, by including a gap-filling strategy, snow estimates could be improved in forested regions where remote sensing is challenging. This approach improved estimates of winter maximum snow water volume to within 4 %, on average, with persistent improvements to both spring snow and runoff in many regions.
Eunsang Cho, Yonghwan Kwon, Sujay V. Kumar, and Carrie M. Vuyovich
Hydrol. Earth Syst. Sci., 27, 4039–4056, https://doi.org/10.5194/hess-27-4039-2023, https://doi.org/10.5194/hess-27-4039-2023, 2023
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An airborne gamma-ray remote-sensing technique provides reliable snow water equivalent (SWE) in a forested area where remote-sensing techniques (e.g., passive microwave) typically have large uncertainties. Here, we explore the utility of assimilating the gamma snow data into a land surface model to improve the modeled SWE estimates in the northeastern US. Results provide new insights into utilizing the gamma SWE data for enhanced land surface model simulations in forested environments.
Wenfu Tang, Louisa K. Emmons, Helen M. Worden, Rajesh Kumar, Cenlin He, Benjamin Gaubert, Zhonghua Zheng, Simone Tilmes, Rebecca R. Buchholz, Sara-Eva Martinez-Alonso, Claire Granier, Antonin Soulie, Kathryn McKain, Bruce C. Daube, Jeff Peischl, Chelsea Thompson, and Pieternel Levelt
Geosci. Model Dev., 16, 6001–6028, https://doi.org/10.5194/gmd-16-6001-2023, https://doi.org/10.5194/gmd-16-6001-2023, 2023
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The new MUSICAv0 model enables the study of atmospheric chemistry across all relevant scales. We develop a MUSICAv0 grid for Africa. We evaluate MUSICAv0 with observations and compare it with a previously used model – WRF-Chem. Overall, the performance of MUSICAv0 is comparable to WRF-Chem. Based on model–satellite discrepancies, we find that future field campaigns in an eastern African region (30°E–45°E, 5°S–5°N) could substantially improve the predictive skill of air quality models.
Eunsang Cho, Carrie M. Vuyovich, Sujay V. Kumar, Melissa L. Wrzesien, and Rhae Sung Kim
The Cryosphere, 17, 3915–3931, https://doi.org/10.5194/tc-17-3915-2023, https://doi.org/10.5194/tc-17-3915-2023, 2023
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As a future snow mission concept, active microwave sensors have the potential to measure snow water equivalent (SWE) in deep snowpack and forested environments. We used a modeling and data assimilation approach (a so-called observing system simulation experiment) to quantify the usefulness of active microwave-based SWE retrievals over western Colorado. We found that active microwave sensors with a mature retrieval algorithm can improve SWE simulations by about 20 % in the mountainous domain.
Cenlin He, Prasanth Valayamkunnath, Michael Barlage, Fei Chen, David Gochis, Ryan Cabell, Tim Schneider, Roy Rasmussen, Guo-Yue Niu, Zong-Liang Yang, Dev Niyogi, and Michael Ek
Geosci. Model Dev., 16, 5131–5151, https://doi.org/10.5194/gmd-16-5131-2023, https://doi.org/10.5194/gmd-16-5131-2023, 2023
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Noah-MP is one of the most widely used open-source community land surface models in the world, designed for applications ranging from uncoupled land surface and ecohydrological process studies to coupled numerical weather prediction and decadal climate simulations. To facilitate model developments and applications, we modernize Noah-MP by adopting modern Fortran code and data structures and standards, which substantially enhance model modularity, interoperability, and applicability.
Zhe Zhang, Yanping Li, Fei Chen, Phillip Harder, Warren Helgason, James Famiglietti, Prasanth Valayamkunnath, Cenlin He, and Zhenhua Li
Geosci. Model Dev., 16, 3809–3825, https://doi.org/10.5194/gmd-16-3809-2023, https://doi.org/10.5194/gmd-16-3809-2023, 2023
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Crop models incorporated in Earth system models are essential to accurately simulate crop growth processes on Earth's surface and agricultural production. In this study, we aim to model the spring wheat in the Northern Great Plains, focusing on three aspects: (1) develop the wheat model at a point scale, (2) apply dynamic planting and harvest schedules, and (3) adopt a revised heat stress function. The results show substantial improvements and have great importance for agricultural production.
Wenfu Tang, Simone Tilmes, David M. Lawrence, Fang Li, Cenlin He, Louisa K. Emmons, Rebecca R. Buchholz, and Lili Xia
Atmos. Chem. Phys., 23, 5467–5486, https://doi.org/10.5194/acp-23-5467-2023, https://doi.org/10.5194/acp-23-5467-2023, 2023
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Globally, total wildfire burned area is projected to increase over the 21st century under scenarios without geoengineering and decrease under the two geoengineering scenarios. Geoengineering reduces fire by decreasing surface temperature and wind speed and increasing relative humidity and soil water. However, geoengineering also yields reductions in precipitation, which offset some of the fire reduction.
Dalei Hao, Gautam Bisht, Karl Rittger, Edward Bair, Cenlin He, Huilin Huang, Cheng Dang, Timbo Stillinger, Yu Gu, Hailong Wang, Yun Qian, and L. Ruby Leung
Geosci. Model Dev., 16, 75–94, https://doi.org/10.5194/gmd-16-75-2023, https://doi.org/10.5194/gmd-16-75-2023, 2023
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Snow with the highest albedo of land surface plays a vital role in Earth’s surface energy budget and water cycle. This study accounts for the impacts of snow grain shape and mixing state of light-absorbing particles with snow on snow albedo in the E3SM land model. The findings advance our understanding of the role of snow grain shape and mixing state of LAP–snow in land surface processes and offer guidance for improving snow simulations and radiative forcing estimates in Earth system models.
Huilin Huang, Yun Qian, Ye Liu, Cenlin He, Jianyu Zheng, Zhibo Zhang, and Antonis Gkikas
Atmos. Chem. Phys., 22, 15469–15488, https://doi.org/10.5194/acp-22-15469-2022, https://doi.org/10.5194/acp-22-15469-2022, 2022
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Using a clustering method developed in the field of artificial neural networks, we identify four typical dust transport patterns across the Sierra Nevada, associated with the mesoscale and regional-scale wind circulations. Our results highlight the connection between dust transport and dominant weather patterns, which can be used to understand dust transport in a changing climate.
Eunsang Cho, Carrie M. Vuyovich, Sujay V. Kumar, Melissa L. Wrzesien, Rhae Sung Kim, and Jennifer M. Jacobs
Hydrol. Earth Syst. Sci., 26, 5721–5735, https://doi.org/10.5194/hess-26-5721-2022, https://doi.org/10.5194/hess-26-5721-2022, 2022
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While land surface models are a common approach for estimating macroscale snow water equivalent (SWE), the SWE accuracy is often limited by uncertainties in model physics and forcing inputs. In this study, we found large underestimations of modeled SWE compared to observations. Precipitation forcings and melting physics limitations dominantly contribute to the SWE underestimations. Results provide insights into prioritizing strategies to improve the SWE simulations for hydrologic applications.
Amy McNally, Jossy Jacob, Kristi Arsenault, Kimberly Slinski, Daniel P. Sarmiento, Andrew Hoell, Shahriar Pervez, James Rowland, Mike Budde, Sujay Kumar, Christa Peters-Lidard, and James P. Verdin
Earth Syst. Sci. Data, 14, 3115–3135, https://doi.org/10.5194/essd-14-3115-2022, https://doi.org/10.5194/essd-14-3115-2022, 2022
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The Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) global and Central Asia data streams described here generate routine estimates of snow, soil moisture, runoff, and other variables useful for tracking water availability. These data are hosted by NASA and USGS data portals for public use.
Chaman Gul, Shichang Kang, Siva Praveen Puppala, Xiaokang Wu, Cenlin He, Yangyang Xu, Inka Koch, Sher Muhammad, Rajesh Kumar, and Getachew Dubache
Atmos. Chem. Phys., 22, 8725–8737, https://doi.org/10.5194/acp-22-8725-2022, https://doi.org/10.5194/acp-22-8725-2022, 2022
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This work aims to understand concentrations, spatial variability, and potential source regions of light-absorbing impurities (black carbon aerosols, dust particles, and organic carbon) in the surface snow of central and western Himalayan glaciers and their impact on snow albedo and radiative forcing.
Min Huang, James H. Crawford, Gregory R. Carmichael, Kevin W. Bowman, Sujay V. Kumar, and Colm Sweeney
Atmos. Chem. Phys., 22, 7461–7487, https://doi.org/10.5194/acp-22-7461-2022, https://doi.org/10.5194/acp-22-7461-2022, 2022
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This study demonstrates that ozone dry-deposition modeling can be improved by revising the model's dry-deposition parameterizations to better represent the effects of environmental conditions including the soil moisture fields. Applying satellite soil moisture data assimilation is shown to also have added value. Such advancements in coupled modeling and data assimilation can benefit the assessments of ozone impacts on human and vegetation health.
Wanshu Nie, Sujay V. Kumar, Kristi R. Arsenault, Christa D. Peters-Lidard, Iliana E. Mladenova, Karim Bergaoui, Abheera Hazra, Benjamin F. Zaitchik, Sarith P. Mahanama, Rachael McDonnell, David M. Mocko, and Mahdi Navari
Hydrol. Earth Syst. Sci., 26, 2365–2386, https://doi.org/10.5194/hess-26-2365-2022, https://doi.org/10.5194/hess-26-2365-2022, 2022
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The MENA (Middle East and North Africa) region faces significant food and water insecurity and hydrological hazards. Here we investigate the value of assimilating remote sensing data sets into an Earth system model to help build an effective drought monitoring system and support risk mitigation and management by countries in the region. We highlight incorporating satellite-informed vegetation conditions into the model as being one of the key processes for a successful application for the region.
Jawairia A. Ahmad, Barton A. Forman, and Sujay V. Kumar
Hydrol. Earth Syst. Sci., 26, 2221–2243, https://doi.org/10.5194/hess-26-2221-2022, https://doi.org/10.5194/hess-26-2221-2022, 2022
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Assimilation of remotely sensed data into a land surface model to improve the spatiotemporal estimation of soil moisture across South Asia exhibits potential. Satellite retrieval assimilation corrects biases that are generated due to an unmodeled hydrologic phenomenon, i.e., irrigation. The improvements in fine-scale, modeled soil moisture estimates by assimilating coarse-scale retrievals indicates the utility of the described methodology for data-scarce regions.
Mark G. Flanner, Julian B. Arnheim, Joseph M. Cook, Cheng Dang, Cenlin He, Xianglei Huang, Deepak Singh, S. McKenzie Skiles, Chloe A. Whicker, and Charles S. Zender
Geosci. Model Dev., 14, 7673–7704, https://doi.org/10.5194/gmd-14-7673-2021, https://doi.org/10.5194/gmd-14-7673-2021, 2021
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We present the technical formulation and evaluation of a publicly available code and web-based model to simulate the spectral albedo of snow. Our model accounts for numerous features of the snow state and ambient conditions, including the the presence of light-absorbing matter like black and brown carbon, mineral dust, volcanic ash, and snow algae. Carbon dioxide snow, found on Mars, is also represented. The model accurately reproduces spectral measurements of clean and contaminated snow.
Cited articles
Abolafia-Rosenzweig, R., Pan, M., Zeng, J. L., and Livneh, B.: Remotely sensed ensembles of the terrestrial water budget over major global river basins: An assessment of three closure techniques, Remote Sensing of Environment, 252, 112191, https://doi.org/10.1016/j.rse.2020.112191, 2021a.
Abolafia-Rosenzweig, R., He, C., Burns, S. P., and Chen, F.: Implementation and evaluation of a unified turbulence parameterization throughout the canopy and roughness sublayer in Noah-MP Snow Simulations, J. Adv. Model. Earth Sy., 13, https://doi.org/10.1029/2021ms002665, 2021b.
Abolafia-Rosenzweig, R., He, C., and Chen, F.: Winter and spring climate explains a large portion of interannual variability and trend in western U.S. summer fire burned area, Environ. Res. Lett., 17, 054030, https://doi.org/10.1088/1748-9326/ac6886, 2022a.
Abolafia-Rosenzweig, R., He, C., McKenzie Skiles, S., Chen, F., and Gochis, D.: Evaluation and optimization of snow albedo scheme in Noah-MP land surface model using in situ spectral observations in the Colorado Rockies, J. Adv. Model. Earth Sy., 14, e2022MS003141, https://doi.org/10.1029/2022MS003141, 2022b.
Abolafia-Rosenzweig, R., He, C., Chen, F., Ikeda, K., Schneider, T., and Rasmussen, R.: High resolution forecasting of summer drought in the western United States, Water Resour. Res., 59, e2022WR033734, https://doi.org/10.1029/2022WR033734, 2023.
Abolafia-Rosenzweig, R., He, C., Chen, F., Zhang, Y., Dugger, A., Livneh, B., and Gochis, D.: Evaluating Noah-MP simulated runoff and snowpack in heavily burned Pacific-Northwest snow-dominated catchments, Journal of Geophysical Research: Atmospheres, 129, e2023JD039780, https://doi.org/10.1029/2023JD039780, 2024a.
Abolafia-Rosenzweig, R., He, C., Chen, F., and Barlage, M.: Evaluating and enhancing snow compaction process in the Noah-MP land surface model, Journal of Advances in Modeling Earth Systems, 16, e2023MS003869, https://doi.org/10.1029/2023MS003869, 2024b.
Abolafia-Rosenzweig, R., He, C., Lin, T. S., Barlage, M., and Rittger, K.: Improved cross-scale snow cover simulations by developing a scale – aware ground snow cover fraction parameterization in the Noah-MP land surface model, J. Adv. Model. Earth Sy., 17, e2024MS004704, https://doi.org/10.1029/2024MS004704, 2025.
AghaKouchak, A., Chiang, F., Huning, L.S., Love, C.A., Mallakpour, I., Mazdiyasni, O., Moftakhari, H., Papalexiou, S.M., Ragno, E., and Sadegh, M.: Climate extremes and compound hazards in a warming world, Annu. Rev. Earth Planet. Sci., 48, 519–548, https://doi.org/10.1146/annurev-earth-071719-055228, 2020.
Arsenault, K. R., Kumar, S. V., Geiger, J. V., Wang, S., Kemp, E., Mocko, D. M., Beaudoing, H. K., Getirana, A., Navari, M., Li, B., Jacob, J., Wegiel, J., and Peters-Lidard, C. D.: The Land surface Data Toolkit (LDT v7.2) – a data fusion environment for land data assimilation systems, Geosci. Model Dev., 11, 3605–3621, https://doi.org/10.5194/gmd-11-3605-2018, 2018.
Arsenault, K. R., Shukla, S., Hazra, A., Getirana, A., McNally, A., Kumar, S. V., Koster, R. D., Peters-Lidard, C. D., Zaitchik, B. F., Badr, H., Jung, H. C., Narapusetty, B., Navari, M., Wang, S., Mocko, D. M., Funk, C., Harrison, L., Husak, G. J., Adoum, A., Galu, G., Magadzire, T., Roningen, J., Shaw, M., Eylander, J., Bergaoui, K., McDonnell, R. A., and Verdin, J. P.: Better Ad- vance Warnings of Drought, B. Am. Meteorol. Soc., 101, 899–903, 2020.
Barlage, M., Chen, F., Rasmussen, R., Zhang, Z., and Miguez- Macho, G.: The importance of scale-dependent groundwa- ter processes in land-atmosphere interactions over the cen- tral United States, Geophys. Res. Lett., 48, e2020GL092171, https://doi.org/10.1029/2020GL092171, 2021.
National Operational Hydrologic Remote Sensing Center: Snow Data Assimilation System (SNODAS) Data Products at NSIDC, G02158, Version 1, National Snow and Ice Data Center [data set], https://doi.org/10.7265/N5TB14TC, 2004.
Benson, D. O. and Dirmeyer, P. A.: The soil moisture–surface flux relationship as a factor for extreme heat predictability in subseasonal to seasonal forecasts, J. Climate, 36, 6375–6392, 2023.
Best, M. J., Pryor, M., Clark, D. B., Rooney, G. G., Essery, R. L. H., Ménard, C. B., Edwards, J. M., Hendry, M. A., Porson, A., Gedney, N., Mercado, L. M., Sitch, S., Blyth, E., Boucher, O., Cox, P. M., Grimmond, C. S. B., and Harding, R. J.: The Joint UK Land Environment Simulator (JULES), model description – Part 1: Energy and water fluxes, Geosci. Model Dev., 4, 677–699, https://doi.org/10.5194/gmd-4-677-2011, 2011.
Bieri, C. A., Dominguez, F., Miguez-Macho, G., and Fan, Y.: Implementing deep soil and dynamic root uptake in Noah-MP (v4.5): impact on Amazon dry-season transpiration, Geosci. Model Dev., 18, 3755–3779, https://doi.org/10.5194/gmd-18-3755-2025, 2025.
Blyth, E. M., Arora, V. K., Clark, D. B., Dadson, S. J., De Kauwe, M. G., Lawrence, D. M., Melton, J. R., Pongratz, J., Turton, R. H., Yoshimura, K., and Yuan, H.: Advances in land surface modelling, Curr. Clim. Change Rep., 7, 45–71, https://doi.org/10.1007/s40641-021-00171-5, 2021
Bonan, G. B.: A land surface model (LSM version 1.0) for ecological, hydrological, and atmospheric studies: Technical description and user's guide, NCAR Tech. Note, NCAR/TN-417+STR, Natl. Cent. for Atmos. Res., Boulder, Colorado, 150 pp., https://doi.org/10.5065/D6DF6P5X, 1996.
Bonan, G. B., Patton, E. G., Harman, I. N., Oleson, K. W., Finnigan, J. J., Lu, Y., and Burakowski, E. A.: Modeling canopy-induced turbulence in the Earth system: a unified parameterization of turbulent exchange within plant canopies and the roughness sublayer (CLM-ml v0), Geosci. Model Dev., 11, 1467–1496, https://doi.org/10.5194/gmd-11-1467-2018, 2018.
Brutsaert, W.: Evaporation into the Atmosphere: Theory, History, and Applications, Springer, Dordrecht, 299 pp., https://doi.org/10.1007/978-94-017-1497-6, 1982.
Chen, F., Janjić, Z., and Mitchell, K.:. Impact of atmospheric surface-layer parameterizations in the new land-surface scheme of the NCEP mesoscale Eta model, Boundary Layer Meteorol., 85, 391–421, 1997.
Collier, N., Hoffman, F. M., Lawrence, D. M., Keppel‐Aleks, G., Koven, C. D., Riley, W. J., Mu, M., and Randerson, J. T.: The International Land Model Benchmarking (ILAMB) system: design, theory, and implementation, J. Adv. Model. Earth Sy., 10, 2731–2754, https://doi.org/10.1029/2018MS001354, 2018.
Dickinson, R. E., Henderson-Sellers, A., and Kennedy, P. J.: Biosphere-Atmosphere Transfer Scheme (BATS) version 1e as coupled to the NCAR Community Climate Model, NCAR Tech. Note, NCAR/TN-387+STR, Natl. Cent. for Atmos. Res., Boulder, Colorado, 80 pp., https://doi.org/10.5065/D67W6959, 1993.
Dorigo, W., Himmelbauer, I., Aberer, D., Schremmer, L., Petrakovic, I., Zappa, L., Preimesberger, W., Xaver, A., Annor, F., Ardö, J., Baldocchi, D., Bitelli, M., Blöschl, G., Bogena, H., Brocca, L., Calvet, J.-C., Camarero, J. J., Capello, G., Choi, M., Cosh, M. C., van de Giesen, N., Hajdu, I., Ikonen, J., Jensen, K. H., Kanniah, K. D., de Kat, I., Kirchengast, G., Kumar Rai, P., Kyrouac, J., Larson, K., Liu, S., Loew, A., Moghaddam, M., Martínez Fernández, J., Mattar Bader, C., Morbidelli, R., Musial, J. P., Osenga, E., Palecki, M. A., Pellarin, T., Petropoulos, G. P., Pfeil, I., Powers, J., Robock, A., Rüdiger, C., Rummel, U., Strobel, M., Su, Z., Sullivan, R., Tagesson, T., Varlagin, A., Vreugdenhil, M., Walker, J., Wen, J., Wenger, F., Wigneron, J. P., Woods, M., Yang, K., Zeng, Y., Zhang, X., Zreda, M., Dietrich, S., Gruber, A., van Oevelen, P., Wagner, W., Scipal, K., Drusch, M., and Sabia, R.: The International Soil Moisture Network: serving Earth system science for over a decade, Hydrol. Earth Syst. Sci., 25, 5749–5804, https://doi.org/10.5194/hess-25-5749-2021, 2021.
Eidhammer, T., Booth, A., Decker, S., Li, L., Barlage, M., Gochis, D., Rasmussen, R., Melvold, K., Nesje, A., and Sobolowski, S.: Mass balance and hydrological modeling of the Hardangerjøkulen ice cap in south-central Norway, Hydrol. Earth Syst. Sci., 25, 4275–4297, https://doi.org/10.5194/hess-25-4275-2021, 2021.
Ek, M. B., Mitchell, K. E., Lin, Y., Rogers, E., Grunmann, P., Koren, V., Gayno, G., and Tarpley, J. D.: Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model, Journal of Geophysical Research: Atmospheres, 108, https://doi.org/10.1029/2002JD003296, 2003.
Eylander, J., Kumar, S., Peters-Lidard, C., Lewiston, T., Franks, C., and Wegiel, J.: History and Development of the USAF Agriculture Meteorology Modeling System and Resulting USAF–NASA Strategic Partnership, Weather and Forecasting, 37, 2293–2312, https://doi.org/10.1175/WAF-D-22-0064.1, 2022.
FAO: The digitized soil map of the world, World Soil Resource Rep. 67, FAO, Rome, https://www.fao.org/soils-portal/data-hub/soil-maps-and-databases/faounesco-soil-map-of-the-world/en/ (last access: 20 October 2025), 1991.
Fisher, R. A. and Koven, C. D.: Perspectives on the future of land surface models and the challenges of representing complex terrestrial systems, Journal of Advances in Modeling Earth Systems, 12, e2018MS001453, https://doi.org/10.1029/2018MS001453, 2020.
Flanner, M. G., Arnheim, J. B., Cook, J. M., Dang, C., He, C., Huang, X., Singh, D., Skiles, S. M., Whicker, C. A., and Zender, C. S.: SNICAR-ADv3: a community tool for modeling spectral snow albedo, Geosci. Model Dev., 14, 7673–7704, https://doi.org/10.5194/gmd-14-7673-2021, 2021.
Hall, D. K. and Riggs, G. A.: MODIS/Terra Snow Cover Daily L3 Global 500m Grid, Version 6. Boulder, Colorado USA, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/MODIS/MOD10A1.006, 2016.
Haverd, V., Smith, B., Nieradzik, L., Briggs, P. R., Woodgate, W., Trudinger, C. M., Canadell, J. G., and Cuntz, M.: A new version of the CABLE land surface model (Subversion revision r4601) incorporating land use and land cover change, woody vegetation demography, and a novel optimisation-based approach to plant coordination of photosynthesis, Geosci. Model Dev., 11, 2995–3026, https://doi.org/10.5194/gmd-11-2995-2018, 2018.
Hazra, A., McNally, A., Slinski, K., Arsenault, K. R., Shukla, S., Getirana, A., Jacob, J. P., Sarmiento, D. P., Peters-Lidard, C., Kumar, S. V., and Koster, R. D.: NASA's NMME- based S2S hydrologic forecast system for food insecurity early warning in southern Africa, J. Hydrol., 617, 129005, https://doi.org/10.1016/j.jhydrol.2022.129005, 2023.
He, C., Chen, F., Barlage, M., Liu, C., Newman, A., Tang, W., Ikeda, K., and Rasmussen, R.: Can convection-permitting modeling provide decent precipitation for offline high-resolution snowpack simulations over mountains, J. Geophys. Res.-Atmos., 124, 12631–12654, https://doi.org/10.1029/2019JD030823, 2019.
He, C., Chen, F., Abolafia-Rosenzweig, R., Ikeda, K., Liu, C., and Rasmussen, R.: What causes the unobserved early-spring snowpack ablation in convection-permitting WRF modeling over Utah mountains?, J. Geophys. Res.-Atmos, 126, e2021JD035284, https://doi.org/10.1029/2021JD035284, 2021.
He, C., Chen, F., Barlage, M., Yang, Z. L., Wegiel, J. W., Niu, G. Y., Gochis, D., Mocko, D. M., Abolafia-Rosenzweig, R., Zhang, Z., Lin, T. S., Valayamkunnath, P., Ek, M., and Niyogi, D.: Enhancing the community Noah-MP land model capabilities for Earth sciences and applications, Bulletin of the American Meteorological Society, 104, E2023–E2029, https://doi.org/10.1175/BAMS-D-23-0249.1, 2023a.
He, C., Valayamkunnath, P., Barlage, M., Chen, F., Gochis, D., Cabell, R., Schneider, T., Rasmussen, R., Niu, G.-Y., Yang, Z.-L., Niyogi, D., and Ek, M.: Modernizing the open-source community Noah with multi-parameterization options (Noah-MP) land surface model (version 5.0) with enhanced modularity, interoperability, and applicability, Geosci. Model Dev., 16, 5131–5151, https://doi.org/10.5194/gmd-16-5131-2023, 2023b.
He, C., Valayamkunnath, P., Barlage, M., Chen, F., Gochis, D., Cabell, R., Schneider, T., Rasmussen, R., Niu, G.-Y., Yang, Z.-L., Niyogi, D., and Ek, M.: The Community Noah-MP Land Surface Modeling System Technical Description Version 5.0, (No. NCAR/TN-575+STR), https://doi.org/10.5065/ew8g-yr95, 2023c.
He, C., Flanner, M., Lawrence, D. M., and Gu, Y.: New features and enhancements in community land model (CLM5) snow albedo modeling: Description, sensitivity, and evaluation, Journal of Advances in Modeling Earth Systems, 16, e2023MS003861, https://doi.org/10.1029/2023MS003861, 2024a.
He, C., Lin, T.-S., and Mocko, D.: LIS/Noah-MPv5.0 coupled source code, Zenodo [code], https://doi.org/10.5281/zenodo.14567646, 2024b.
He, C., Lin, T.-S., and Mocko, D.: LIS/Noah-MP model benchmark and evaluation dataset, Zenodo [data set], https://doi.org/10.5281/zenodo.14567220, 2025.
Jiang, Y., Chen, F., Gao, Y., He, C., Barlage, M., and Huang, W.: As- sessment of uncertainty sources in snow cover simulation in the Tibetan Plateau, J. Geophys. Res.-Atmos., 125, e2020JD032674, https://doi.org/10.1029/2020JD032674, 2020.
Jordan, R.: A one-dimensional temperature model for a snow cover, Spec. Rep. 91–16, Cold Reg. Res. and Eng. Lab., U.S. Army Corps. of Eng., Hanover, N. H., https://hdl.handle.net/11681/11677 (last access: 20 October 2025), 1991.
Kemp, E., Wegiel, J. W., Kumar, S. V., Geiger, J. V., Mocko, D. M., Jacob, J., and Peters-Lidard, C. D.: A NASA-Air Force precipitation analysis for near-real-time Ooerations, J. Hydrometeorol., 23, 965–989, https://doi.org/10.1175/JHM-D-21-0228.1, 2022.
Koster, R. D. and G. K. Walker: Interactive Vegetation Phenology, Soil Moisture, and Monthly Temperature Forecasts, J. Hydrometeorol., 16, 1456–1465, 2015.
Kumar, S. V., Peters-Lidard, C. D., Tian, Y., Houser, P. R., Geiger, J., Olden, S., Lighty, L., Eastman, J. L., Doty, B., Dirmeyer, P., Adams, J., Mitchell, K., Wood, E. F., and Sheffield, J.: Land Information System – An interoperable framework for high resolution land surface modeling, Environ. Modeling & Software, 21, 1402–1415, https://doi.org/10.1016/j.envsoft.2005.07.004, 2006.
Kumar, S. V., Reichle, R. H., Peters-Lidard, C. D., Koster, R. D., Zhan, X., Crow, W. T., Eylander, J. B., and Houser, P. R.: A land surface data assimilation framework using the land information system: Description and applications, Advances in Water Resources, 31, 1419–1432, https://doi.org/10.1016/j.advwatres.2008.01.013, 2008a.
Kumar, S. V., Peters-Lidard, C. D., Eastman, J. L., and Tao, W.-K.: An integrated high resolution hydrometeorological modeling testbed using LIS and WRF, Environ. Modelling & Software, 23, 169–181, https://doi.org/10.1016/j.envsoft.2007.05.012, 2008b.
Kumar, S. V., Peters-Lidard, C. D., Santanello, J., Harrison, K., Liu, Y., and Shaw, M.: Land surface Verification Toolkit (LVT) – a generalized framework for land surface model evaluation, Geosci. Model Dev., 5, 869–886, https://doi.org/10.5194/gmd-5-869-2012, 2012.
Kumar, S. V., Zaitchik, B. F., Peters-Lidard, C. D., Rodell, M., Reichle, R., Li, B., Jasinski, M., Mocko, D., Getirana, A., De Lannoy, G., Cosh, M. H., Hain, C. R., Anderson, M., Arsenault, K. R., Xia, Y., and Ek, M.: Assimilation of gridded GRACE terrestrial water storage estimates in the North American Land Data Assimilation System, Journal of Hydrometeorology, 17, 1951–1972, https://doi.org/10.1175/JHM-D-15-0157.1, 2016.
Kumar, S. V., M. Mocko, D., Wang, S., Peters-Lidard, C. D., and Borak, J.: Assimilation of remotely sensed Leaf Area Index into the Noah-MP land surface model: Impacts on water and carbon fluxes and states over the Continental U.S, Journal of Hydrometeorology, 20, 1359–1377, https://doi.org/10.1175/jhm-d-18-0237.1, 2019.
Kumar, S. V., Mocko, D. M., Vuyovich, C., and Peters-Lidard, C. D.: Impact of surface albedo assimilation on snow estimation, Remote Sens., 12, 645, https://doi.org/10.3390/rs12040645, 2020.
Kumar, S. V., Holmes, T., Andela, N., Dharssi, I., Vinodku- mar, Hain, C., Peters-Lidard, C., Mahanama, S. P., Arsenault, K. R., Nie, W., and Getirana, A.: The 2019–2020 Aus- tralian drought and bushfires altered the partitioning of hydrological fluxes, Geophys. Res. Lett., 48, e2020GL091411, https://doi.org/10.1029/2020GL091411, 2021.
Lawrence, P. J. and Chase, T. N.: Representing a new MODIS consistent land surface in the Community Land Model (CLM 3.0), J. Geophys. Res. Biogeosci., 112, https://doi.org/10.1029/2006JG000168, 2007.
Li, J., Chen, F., Lu, X., Gong, W., Zhang, G., and Gan, Y.: Quantifying contributions of uncertainties in physical parameterization schemes and model parameters to overall errors in Noah-MP dynamic vegetation modeling, J. Adv. Model. Earth Sy., 12, e2019MS001914, https://doi.org/10.1029/2019MS001914, 2020.
Li, L., Yang, Z. L., Matheny, A. M., Zheng, H., Swenson, S. C., Lawrence, D. M., Barlage, M., Yan, B., McDowell, N. G., and Leung, L. R.: Representation of plant hydraulics in the Noah-MP land surface model: Model development and multiscale evaluation, J. Adv. Model. Earth Sy., 13, e2020MS002214, https://doi.org/10.1029/2020MS002214, 2021.
Li, L., Bisht, G., Hao, D., and Leung, L. R.: Global 1 km land surface parameters for kilometer-scale Earth system modeling, Earth Syst. Sci. Data, 16, 2007–2032, https://doi.org/10.5194/essd-16-2007-2024, 2024.
Liang, X., Lettenmaier, D. P., Wood, E. F., and Burges, S. J.: A simple hydrologically based model of land surface water and energy fluxes for general circulation models, J. Geophys. Res., 99, 14415–14428, https://doi.org/10.1029/94JD00483, 1994.
Lin, T.-S., He, C., Abolafia-Rosenzweig, R., Chen, F., Wang, W., Barlage, M., and Gochis, D.: Improved snow albedo evolution in Noah-MP land surface model coupled with a physical snowpack radiative transfer scheme, J. Hydrometeorol., 26, 185–200, https://doi.org/10.1175/JHM-D-24-0082.1, 2025
Liu, C., Ikeda, K., Rasmussen, R., Barlage, M., Newman, A. J., Prein, A. F., Chen, F., Chen, L., Clark, M., Dai, A., Dudhia, J., Eidhammer, T., Gochis, D., Gutmann, E., Kurkute, S., Li, Y., Thompson, G., and Yates, D.: Continental- scale convection-permitting modeling of the current and fu- ture climate of North America, Clim. Dynam., 49, 71–95, https://doi.org/10.1007/s00382-016-3327-9, 2017.
Liu, Y., Peters-Lidard, C. D., Kumar, S. V., Arsenault, K. R., and Mocko, D. M.: Blending satellite-based snow depth products with in situ observations for streamflow predictions in the Upper Colorado River Basin, Water Resour. Res., 51, 1182–1202, https://doi.org/10.1002/2014WR016606, 2015.
Miralles, D. G., Holmes, T. R. H., De Jeu, R. A. M., Gash, J. H., Meesters, A. G. C. A., and Dolman, A. J.: Global land-surface evaporation estimated from satellite-based observations, Hydrol. Earth Syst. Sci., 15, 453–469, https://doi.org/10.5194/hess-15-453-2011, 2011.
Monteiro, D. and Morin, S.: Multi-decadal analysis of past winter temperature, precipitation and snow cover data in the European Alps from reanalyses, climate models and observational datasets, The Cryosphere, 17, 3617–3660, https://doi.org/10.5194/tc-17-3617-2023, 2023.
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.: ERA5-Land: a state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, 2021.
Navari, M., Kumar, S., Wang, S., Geiger, J., Mocko, D. M., Arsenault, K. R., and Kemp, E. M.: Enabling advanced snow physics within land surface models through an interoperable model-physics coupling framework, J. Adv. Model. Earth Sy., 16, e2022MS003236, https://doi.org/10.1029/2022MS003236, 2024.
Nelson, J. A., Walther, S., Gans, F., Kraft, B., Weber, U., Novick, K., Buchmann, N., Migliavacca, M., Wohlfahrt, G., Šigut, L., Ibrom, A., Papale, D., Göckede, M., Duveiller, G., Knohl, A., Hörtnagl, L., Scott, R. L., Dušek, J., Zhang, W., Hamdi, Z. M., Reichstein, M., Aranda-Barranco, S., Ardö, J., Op de Beeck, M., Billesbach, D., Bowling, D., Bracho, R., Brümmer, C., Camps-Valls, G., Chen, S., Cleverly, J. R., Desai, A., Dong, G., El-Madany, T. S., Euskirchen, E. S., Feigenwinter, I., Galvagno, M., Gerosa, G. A., Gielen, B., Goded, I., Goslee, S., Gough, C. M., Heinesch, B., Ichii, K., Jackowicz-Korczynski, M. A., Klosterhalfen, A., Knox, S., Kobayashi, H., Kohonen, K.-M., Korkiakoski, M., Mammarella, I., Gharun, M., Marzuoli, R., Matamala, R., Metzger, S., Montagnani, L., Nicolini, G., O'Halloran, T., Ourcival, J.-M., Peichl, M., Pendall, E., Ruiz Reverter, B., Roland, M., Sabbatini, S., Sachs, T., Schmidt, M., Schwalm, C. R., Shekhar, A., Silberstein, R., Silveira, M. L., Spano, D., Tagesson, T., Tramontana, G., Trotta, C., Turco, F., Vesala, T., Vincke, C., Vitale, D., Vivoni, E. R., Wang, Y., Woodgate, W., Yepez, E. A., Zhang, J., Zona, D., and Jung, M.: X-BASE: the first terrestrial carbon and water flux products from an extended data-driven scaling framework, FLUXCOM-X, Biogeosciences, 21, 5079–5115, https://doi.org/10.5194/bg-21-5079-2024, 2024.
Nie, W., Kumar, S. V., Getirana, A., Zhao, L., Wrzesien, M., Konapala, G., Ahmad, S., Locke, K., Holmes, T., Loomis, B., and Rodell, M.: Nonstationarity in the global terrestrial water cycle and its interlinkages in the Anthropocene, P. Natl. Acad. Sci. USA, 121, e2403707121, https://doi.org/10.1073/pnas.2403707121, 2024
Niu, G.-Y. and Yang, Z.-L.: The effects of canopy processes on snow surface energy and mass balances, J. Geophys. Res.-Atmos., 109, D23111, https://doi.org/10.1029/2004JD004884, 2004.
Niu, G.-Y. and Yang, Z.-L.: Effects of frozen soil on snowmelt runoff and soil water storage at a continental scale, J. Hydrometeorol., 7, 937–952, https://doi.org/10.1175/JHM538.1, 2006.
Niu, G. Y., Yang, Z. L., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M., Kumar, A., Manning, K., Niyogi, D., Rosero, E., Tewari, M., and Xia, Y.: The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements, J. Geophys. Res.-Atmos., 116, D12109, https://doi.org/10.1029/2010JD015139, 2011.
Niu, G. Y., Fang, Y. H., Chang, L. L., Jin, J., Yuan, H., and Zeng, X.: Enhancing the Noah-MP ecosystem response to droughts with an explicit representation of plant water storage supplied by dynamic root water uptake, J. Adv. Model. Earth Sy., 12, e2020MS002062, https://doi.org/10.1029/2020MS002062, 2020.
O'Neill, P. E., Chan, S., Njoku, E. G., Jackson, T., Bindlish, R., and Chaubell, J.: SMAP L3 Radiometer Global Daily 36 km EASE-Grid Soil Moisture. (SPL3SMP, Version 8), Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/OMHVSRGFX38O, 2021.
Oleson, K., Dai, Y., Bonan, B., Bosilovichm, M., Dickinson, R., Dirmeyer, P., Hoffman, F., Houser, P., Levis, S., Niu, G. Y., Thornton, P., Vertenstein, M., Yang, Z. L., and Zeng, X.: Technical description of the Community Land Model (CLM), NCAR Tech. Note, NCAR/TN-461+STR, Natl. Cent. for Atmos. Res., Boulder, Colo., 174 pp., https://doi.org/10.5065/D6N877R0, 2004.
Peters-Lidard, C. D., Houser, P. R., Tian, Y., Kumar, S. V., Geiger, J., Olden, S., Lighty, L., Doty, B., Dirmeyer, P., Adams, J., Mitchell, K., Wood, E. F., and Sheffield, J.: High-performance Earth system modeling with NASA/GSFC's Land Information System, Innovations in Systems and Software Engineering, 3, 157–165, https://doi.org/10.1007/s11334-007-0028-x, 2007.
Rasmussen, R., Chen, F., Liu, C., Ikeda, K., Prein, A., Kim, J.-H., Schneider, T., Dai, A., Gochis, D., Dugger, A., Zhang, Y., Jaye, A., Dudhia, J., He, C., Harrold, M., Xue, L., Chen, S., New- man, A., Dougherty, E., Abolafia-Rosenzweig, R., Lybarger, N., Viger, R., Lesmes, D. P., Skalak, K., Brakebill, J. W., Clline, D., Dunne, K., Rasmussen, K., and Miguez-Macho, G.: CONUS404: The NCAR-USGS 4-km long-term regional hydroclimate reanal- ysis over the CONUS, B. Am. Meteorol. Soc., E1382–E1408, https://doi.org/10.1175/BAMS-D-21-0326.1, 2023.
Sakaguchi, K. and Zeng, X.: Effects of soil wetness, plant litter, and under-canopy atmospheric stability on ground evaporation in the Community Land Model (CLM3.5), J. Geophys. Res.-Atmos., 114, https://doi.org/10.1029/2008JD010834, 2009.
Santanello, J. A., Kumar, S. V., Peters-Lidard, C. D., and Lawston, P. M.: Impact of soil moisture assimilation on land surface model spinup and coupled land–atmosphere prediction, J. Hydrometeorol., 17, 517–540, https://doi.org/10.1175/JHM-D-15-0072.1, 2016.
Schaake, J. C., Koren, V. I., Duan, Q. Y., Mitchell, K., and Chen, F.: Simple water balance model for estimating runoff at different spatial and temporal scales, J. Geophys. Res.-Atmos., 101, 7461–7475, https://doi.org/10.1029/95JD02892, 1996.
Schaaf, C. and Wang, Z.: MODIS/Terra+Aqua BRDF/Albedo Albedo Daily L3 Global 0.05Deg CMG V061, NASA EOSDIS Land Processes Distributed Active Archive Center [data set], https://doi.org/10.5067/MODIS/MCD43C3.061, 2021.
Sillmann, J., Thorarinsdottir, T., Keenlyside, N., Schaller, N., Alexander, L.V., Hegerl, G., Seneviratne, S. I., Vautard, R., Zhang, X., and Zwiers, F.W.: Understanding, modeling and predicting weather and climate extremes: Challenges and opportunities, Weather Clim. Extrem., 18, 65–74, https://doi.org/10.1016/j.wace.2017.10.003, 2017.
Sun, S., Li, L., Yang, Z. L., Wang, G., McDowell, N. G., Matheny, A. M., Wu, J., Xu, S., Zheng, H., Yu, M., and Wang, D.: Refining water and carbon fluxes modeling in terrestrial ecosystems via plant hydraulics integration, Agr. Forest Meteorol., 359, 110256, https://doi.org/10.1016/j.agrformet.2024.110256, 2024.
Wu, D., Peters-Lidard, C., Tao, W. K., and Petersen, W.: Evaluation of NU-WRF rainfall forecasts for IFloodS, J. Hydrometeorol., 17, 1317–1335, 2016.
Xia, Y., Mitchell, K., Ek, M., Sheffield, J., Cosgrove, B., Wood, E., Luo, L., Alonge, C., Wei, H., Meng, J., and Livneh, B.: Continental-scale water and energy flux analysis and validation for the North American Land Data Assimilation System project phase 2 (NLDAS-2): 1. Intercomparison and application of model products, J. Geophys. Res.-Atmos., 117, https://doi.org/10.1029/2011JD016048, 2012.
Xu, T., Chen, F., He, X., Barlage, M., Zhang, Z., Liu, S., and He, X.: Improve the performance of the noah-MP-crop model by jointly assimilating soil moisture and vegetation phenol- ogy data, J. Adv. Model. Earth Sy., 13, e2020MS002394, https://doi.org/10.1029/2020MS002394, 2021.
Xue, L., Doan, Q. V., Kusaka, H., He, C., and Chen, F.: Insights into urban heat island and heat waves synergies revealed by a Land-Surface-Physics-Based Downscaling method, J. Geophys. Res.-Atmos., 129, e2023JD040531, https://doi.org/10.1029/2023JD040531, 2024.
Xue, L., Doan, Q.-V., Kusaka, H., He, C., and Chen, F.: Land-Surface-Physics-Based Downscaling versus Conventional Dynamical Downscaling for High-Resolution Urban Climate Change Information: The Case Study of Two Cities, Urban Clim., 59, 102228, https://doi.org/10.1016/j.uclim.2024.102228, 2025.
Yang, Z.-L., Niu, G.-Y., Mitchell, K.E., Chen, F., Ek, M.B., Barlage, M., Longuevergne, L., Manning, K., Niyogi, D., Tewari, M., and Xia, Y.: The community Noah land surface model with multiparameterization options (Noah-MP): 2. Evaluation over global river basins, J. Geophys. Res. 116, D12110, https://doi.org/10.1029/2010JD015140, 2011.
Yen, Y. C.: Effective thermal conductivity and water vapor diffusivity of naturally compacted snow, J. Geophys. Res.-Atmos., 70, 1821–1825, https://doi.org/10.1029/JZ070i008p01821, 1965.
Zhang, Z., Li, Y., Chen, F., Harder, P., Helgason, W., Famiglietti, J., Valayamkunnath, P., He, C., and Li, Z.: Developing spring wheat in the Noah-MP land surface model (v4.4) for growing season dynamics and responses to temperature stress, Geosci. Model Dev., 16, 3809–3825, https://doi.org/10.5194/gmd-16-3809-2023, 2023.
Zhang, Z., He, C., Chen, F., Miguez-Macho, G., Liu, C., and Rasmussen, R.: US Corn Belt enhances regional precipitation recycling, P. Natl. Acad. Sci. USA, 122, e2402656121, https://doi.org/10.1073/pnas.2402656121, 2025.
Zhou, X., Ding, B., Yang, K., Pan, J., Ma, X., Zhao, L., Li, X., and Shi, J.: Reducing the Cold Bias of the WRF Model Over the Tibetan Plateau by Implementing a Snow Coverage-Topography Relationship and a Fresh Snow Albedo Scheme, J. Adv. Model. Earth Sy., 15, e2023MS003626, https://doi.org/10.1029/2023MS003626, 2023.
Short summary
This study integrates the refactored community Noah-MP version 5.0 model with the NASA Land Information System (LIS) version 7.5.2 to streamline the synchronization, development, and maintenance of Noah-MP within LIS and to enhance their interoperability and applicability. The model benchmarking and evaluation results reveal key model strengths and weaknesses in simulating land surface quantities and show implications for future model improvements.
This study integrates the refactored community Noah-MP version 5.0 model with the NASA Land...
