Improve the Performance of the Noah‐MP‐Crop Model by Jointly Assimilating Soil Moisture and Vegetation Phenology Data

Xu, Tongren; Chen, Fei; He, Xinlei; Barlage, Michael; Zhang, Zhe; Liu, Shaomin; He, Xiangping

doi:10.1029/2020ms002394

Cited by 31 publications

(15 citation statements)

References 97 publications

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“…These studies also found that the impact of GRACE data assimilation (DA) on snow, runoff, and ET are mixed, partly due to the misrepresentation in model processes 56,57 , requiring either an improvement in model representation of processes that affect the water and energy partitioning 58 or additional constraints for key model states. Here we employ the latter strategy through the joint assimilation of vegetation and surface soil moisture conditions [59][60][61] along with TWS. Assimilating remotely sensed vegetation conditions, such as leaf area index (LAI), and vegetation optical depth has been shown to improve the estimates of terrestrial carbon fluxes, the associated soil moisture, ET, runoff responses, and the representation of extreme events such as wildfires and droughts [62][63][64] .…”

Section: Datasets For Data Assimilationmentioning

confidence: 99%

Nonstationarity in the global terrestrial water cycle and its interlinkages in the Anthropocene

Nie

Kumar²,

Getirana

et al. 2023

Preprint

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Warming climate and anthropogenic disturbances alter the global freshwater cycle, causing changes and fluctuations beyond what can be attributed to natural variability alone. A comprehensive understanding of nonstationarity in water cycle components, where trends and fluctuations deviate from the assumption of a stable statistical pattern, remains elusive. Using a remote sensing-informed terrestrial reanalysis, we provide an assessment of nonstationarity in global water cycle components and their interconnections from 2003 to 2020 in terms of trends, seasonal shifts, and changes in extremes. We show that one-third of the global land exhibits strong nonstationarity in terrestrial water storage, with long-term trends contributing the most (47.3%), followed by seasonal shifts (36.2%) and changes in extremes (16.5%). The spatial pattern of nonstationarity in other water cycle variables confirm the intricate interplay of climate variations and human management processes. Our results provide a global quantification of the pervasiveness of nonstationarity in the terrestrial water cycle and highlight the need for adaptive strategies and management approaches to navigate the challenges posed by nonstationary conditions.

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Section: Datasets For Data Assimilationmentioning

confidence: 99%

Nonstationarity in the global terrestrial water cycle and its interlinkages in the Anthropocene

Nie

Kumar²,

Getirana

et al. 2023

Preprint

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“…It should also be noted that a large part of the RMSE is due to bias at most of these stations. Nevertheless, the advantage of SM-DA and LAI-DA on the land surface process variables is well established in prior studies (Albergel et al, 2010;Kolassa et al, 2017;Kumar et al, 2014Lievens et al, 2017;Liu et al, 2011;Xu et al, 2021).…”

Section: Multivariate Da Captures Spatial Signatures Of Flash Drought...mentioning

confidence: 99%

Flash Drought Onset and Development Mechanisms Captured With Soil Moisture and Vegetation Data Assimilation

Ahmad

Kumar

Lahmers

et al. 2022

Water Resources Research

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Flash droughts evolve and intensify rapidly under the influence of anomalous atmospheric conditions. In this study, we investigate the role of assimilating remotely sensed soil moisture (SM) and vegetation properties in capturing the evolution and impacts of two flash droughts in the Northern Great Plains. We find that during 2016 drought triggered by anomalously high temperatures and excessive evaporative demands, multivariate data assimilation (DA) of MODIS‐derived leaf area index (LAI) and Soil Moisture Active Passive SM within Noah‐Multiparameterization model helps capture elevated transpiration at onset. Assimilation of LAI particularly helped model the resulting rapid decline in SM during onset with as high as 10.0% steeper rate of decline compared to the simulation without any assimilation. Modeled‐SM anomalies exhibit a 7.5% and 11.7% increase in similarity with Evaporative Stress Index (ESI) data and U.S. Drought Monitor (USDM) maps, respectively. In contrast, during 2017 flash drought driven by record‐low precipitation during summers, SM assimilation resulted in largest rates of decline in rootzone SM, as large as 48.4% compared to results from no assimilation. Multivariate DA of SM and LAI results in 6.7% and 14.3% higher spatial similarity with ESI and USDM, respectively, and is necessary to model rapid intensification caused by anomalous precipitation deficits. This study elucidates the need to incorporate multiple observational constraints from remote sensing to effectively capture rapid onset rates, intensification, and severity of flash drought following different propagation mechanisms. This is fundamental for drought early detection to provide a wider window of response and implement efficient mitigation strategies.

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“…Noah-MP can be applied to various spatial scales spanning from point scale locally to ~100-km resolution globally, and temporal scales spanning from sub-daily to decadal time scales. Since its original development, Noah-MP has been used in many important applications, including numerical weather prediction (Suzuki and Zupanski, 2018;Ju et al, 2022), high-resolution climate modeling (Gao et al, 2017;Liu et al, 2017;Rasmussen et al, 2023), land data assimilation (Xu et al, 2021;Nie et al, 2022), drought (Arsenault et al, 2020;Niu et al, 2020;Wu et al, 2021;Abolafia-Rosenzweig et al, 2023a), wildfire (Kumar et al, 2021;Abolafia-Rosenzweig et al, 2022a, 2023b, snowpack evolution (Wrzesien et al, 2015;He et al, 2019;Jiang et al, 2020), hydrology and water resources (Cai et al, 2014;Liang et al, 2019;X. Zhang et al, 2022a;Hazra et al, 2023), crop and agricultural management (Liu et al, 2016;Ingwersen et al, 2018;Warrach-Sagi et al, 2022;Valayamkunnath et al, 2022;Zhang et al, 2020Zhang et al, , 2023, urbanization and heat island (Xu et al, 2018;Salamanca et al, 2018;Patel et al, 2022), biogeochemical cycle (Cai et al, 2016;Brunsell et al, 2021), wind erosion (Jiang et al, 2021), wetland (Z.…”

Section: Introductionmentioning

confidence: 99%

Modernizing the open-source community Noah-MP land surface model (version 5.0) with enhanced modularity, interoperability, and applicability

Valayamkunnath

Barlage³

et al. 2023

Preprint

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Abstract. The widely-used open-source community Noah-MP land surface model (LSM) is designed for applications ranging from uncoupled land-surface and ecohydrological process studies to coupled numerical weather prediction and decadal global/regional climate simulations. It has been used in many coupled community weather/climate/hydrology models. In this study, we modernize/refactor the Noah-MP LSM by adopting modern Fortran code and data structures and standards, which substantially enhances the model modularity, interoperability, and applicability. The modernized Noah-MP is released as the version 5.0 (v5.0), which has five key features: (1) enhanced modularization and interoperability by re-organizing model physics into individual process-level Fortran module files, (2) enhanced data structure with new hierarchical data types and optimized variable declaration and initialization structures, (3) enhanced code structure and calling workflow by leveraging the new data structure and modularization, (4) enhanced (descriptive and self-explanatory) model variable naming standard, and (5) enhanced driver and interface structures to couple with host weather/climate/hydrology models. In addition, we create a comprehensive technical documentation of the Noah-MP v5.0 and a set of model benchmark and reference datasets. The Noah-MP v5.0 will be coupled to various weather/climate/hydrology models in the future. Overall, the modernized Noah-MP will allow a more efficient and convenient process for future model developments and applications.

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Improve the Performance of the Noah‐MP‐Crop Model by Jointly Assimilating Soil Moisture and Vegetation Phenology Data

Abstract: Accurate predictions of water and carbon fluxes in croplands are essential for determining crop yield, hydrologic components, and irrigation schedules and researching climate change (

Cited by 31 publications

References 97 publications

Nonstationarity in the global terrestrial water cycle and its interlinkages in the Anthropocene

Nonstationarity in the global terrestrial water cycle and its interlinkages in the Anthropocene

Flash Drought Onset and Development Mechanisms Captured With Soil Moisture and Vegetation Data Assimilation

Modernizing the open-source community Noah-MP land surface model (version 5.0) with enhanced modularity, interoperability, and applicability

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