Impacts of land cover heterogeneity and land surface parameterizations on turbulent characteristics and mesoscale simulations

Zheng, Yue; Brunsell, Nathaniel A.; Alfieri, Joseph G.; Niyogi, Dev

doi:10.1007/s00703-020-00768-9

Cited by 4 publications

(5 citation statements)

References 86 publications

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“…When wet SM hotspots are resolved at spatial scales closer to their true spatial variability, they enhance evapotranspiration to the atmosphere in comparison to spatially homogeneous drier conditions (Crow & Wood, 2002; Rouholahnejad Freund et al., 2020). This higher evapotranspiration further cools the ground surface, and can enhance horizontal atmospheric humidity and temperature gradients that drive variable boundary layer dynamics, development of large‐scale eddies, potentially triggering of local convective rainfall (Ford et al., 2015; Simon et al., 2021; Vergopolan & Fisher, 2016; Zheng et al., 2021). Spatially variable SM also controls plant photosynthesis rates and nutrient cycling, yielding non‐linear changes of 40%–80% in carbon uptake (Green et al., 2019; Trugman et al., 2018) and 78% nitrogen cycling (Paul et al., 2003).…”

Section: Discussionmentioning

confidence: 99%

“…For instance, SM hotspots influence freshwater sources and agricultural management, as wet and dry conditions require different irrigation and fertilizer interventions for optimal crop growth (Franz et al., 2020; Sadri et al., 2020; Vergopolan et al., 2021). SM spatial variability leads to changes in surface temperature and evapotranspiration (Rouholahnejad Freund et al., 2020), altering drought impacts (Vergopolan et al., 2021) as well as the formation of clouds and convective storms (Simon et al., 2021; Zheng et al., 2021). SM hotspots can alter runoff generation, resulting in faster and peakier flood events (Zhu et al., 2018), and trigger wildfires (Holden et al., 2019; Taufik et al., 2017) and landslides (Brocca et al., 2016; Wang et al., 2020).…”

Section: Introductionmentioning

confidence: 99%

“…, altering drought impacts (Vergopolan et al, 2021) as well as the formation of clouds and convective storms (Simon et al, 2021;Zheng et al, 2021). SM hotspots can alter runoff generation, resulting in faster and peakier flood events (Zhu et al, 2018), and trigger wildfires (Holden et al, 2019;Taufik et al, 2017) and landslides (Brocca et al, 2016;Wang et al, 2020).…”

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confidence: 99%

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High‐Resolution Soil Moisture Data Reveal Complex Multi‐Scale Spatial Variability Across the United States

Vergopolan

Sheffield

Chaney

et al. 2022

Geophysical Research Letters

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Soil moisture (SM) spatiotemporal variability critically influences water resources, agriculture, and climate. However, besides site‐specific studies, little is known about how SM varies locally (1–100‐m scale). Consequently, quantifying the SM variability and its impact on the Earth system remains a long‐standing challenge in hydrology. We reveal the striking variability of local‐scale SM across the United States using SMAP‐HydroBlocks — a novel satellite‐based surface SM data set at 30‐m resolution. Results show how the complex interplay of SM with landscape characteristics and hydroclimate is primarily driven by local variations in soil properties. This local‐scale complexity yields a remarkable and unique multi‐scale behavior at each location. However, very little of this complexity persists across spatial scales. Experiments reveal that on average 48% and up to 80% of the SM spatial information is lost at the 1‐km resolution, with complete loss expected at the scale of current state‐of‐the‐art SM monitoring and modeling systems (1–25 km resolution).

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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High‐Resolution Soil Moisture Data Reveal Complex Multi‐Scale Spatial Variability Across the United States

Vergopolan

Sheffield

Chaney

et al. 2022

Geophysical Research Letters

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“…Detailed and accurate information on the spatiotemporal distribution of soil moisture is important for numerous applications, such as monitoring of drought [1][2][3] and crop irrigation demands [4][5][6] ; mapping antecedent conditions that trigger wildfires 7,8 , landslides 9,10 , and flooding 11,12 ; and quantifying water, energy, and carbon fluxes between the land and atmosphere [13][14][15] . Depending on the landscape heterogeneity, such physical processes can occur at the 1-100 m spatial scale, at which in-situ sensors could provide detailed information.…”

Section: Background and Summarymentioning

confidence: 99%

SMAP-HydroBlocks, a 30-m satellite-based soil moisture dataset for the conterminous US

et al. 2021

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Soil moisture plays a key role in controlling land-atmosphere interactions, with implications for water resources, agriculture, climate, and ecosystem dynamics. Although soil moisture varies strongly across the landscape, current monitoring capabilities are limited to coarse-scale satellite retrievals and a few regional in-situ networks. Here, we introduce SMAP-HydroBlocks (SMAP-HB), a high-resolution satellite-based surface soil moisture dataset at an unprecedented 30-m resolution (2015–2019) across the conterminous United States. SMAP-HB was produced by using a scalable cluster-based merging scheme that combines high-resolution land surface modeling, radiative transfer modeling, machine learning, SMAP satellite microwave data, and in-situ observations. We evaluated the resulting dataset over 1,192 observational sites. SMAP-HB performed substantially better than the current state-of-the-art SMAP products, showing a median temporal correlation of 0.73 ± 0.13 and a median Kling-Gupta Efficiency of 0.52 ± 0.20. The largest benefit of SMAP-HB is, however, the high spatial detail and improved representation of the soil moisture spatial variability and spatial accuracy with respect to SMAP products. The SMAP-HB dataset is available via zenodo and at https://waterai.earth/smaphb.

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“…Sub‐grid heterogeneity comprises spatial variabilities in land use/land cover (LULC) types, soil characteristics, and topography (Giorgi & Avissar, 1997). Sub‐grid heterogeneity affects the surface energy partitioning, modifies the vertical structure of the planetary boundary layer (PBL), creates mesoscale atmospheric circulations, and affects cloud formation and regional climate (Brunsell et al., 2011; Chen et al., 2020; Lee, Zhang, & Klein, 2019; Maronga & Raasch, 2013; Wu et al., 2009; Zhang et al., 2010; Zheng et al., 2021). Accounting for sub‐grid heterogeneities of land surface plays a vital role in land surface modeling and land‐atmosphere coupling (de Vrese et al., 2016; Fisher & Koven, 2020).…”

Section: Introductionmentioning

confidence: 99%

Impacts of Sub‐Grid Topographic Representations on Surface Energy Balance and Boundary Conditions in the E3SM Land Model: A Case Study in Sierra Nevada

Hao

Bisht

Huang

et al. 2022

J Adv Model Earth Syst

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Sub‐grid topographic heterogeneity has large impacts on surface energy balance and land‐atmosphere interactions. However, the impacts of representing sub‐grid topographic effects in land surface models (LSMs) on surface energy balance and boundary conditions remain unclear. This study analyzed and evaluated the impacts of sub‐grid topographic representations on surface energy balance, turbulent heat flux, and scalar (co‐)variances in the Energy Exascale Earth System Model (E3SM) land model (ELM). Three sub‐grid topographic representations in ELM were compared: (a) the default sub‐grid structure (D), (b) the recently developed sub‐grid topographic structure (T), and (c) high spatial resolution (1KM). Additionally, two different solar radiation schemes in ELM were compared: (a) the default plane‐parallel radiative transfer scheme (PP) and (b) the parameterization scheme (TOP) that accounts for sub‐grid topographic effects on solar radiation. A series of offline simulations with the three grid discretization structures (D, T, and 1KM) and two schemes of solar radiation (TOP and PP) were carried out using the Sierra Nevada, California. 1KM simulations with TOP well capture the spatial heterogeneity of surface fluxes compared to Moderate Resolution Imaging Spectroradiometer remote sensing data. There are significant differences between TOP and PP in the 1‐km simulated surface energy balance, but the differences in mean values and standard deviations become small when aggregated to the grid scale (i.e., 0.5°). The T configuration better mimics the 1KM simulations with TOP than the D configuration and better captures the sub‐grid topographic effects on surface energy balance and boundary conditions. These results underline the importance of representing sub‐grid topographic heterogeneities in LSMs and motivate future research to understand the sub‐grid topographic effects on land‐atmosphere interactions over mountainous areas.

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Impacts of land cover heterogeneity and land surface parameterizations on turbulent characteristics and mesoscale simulations

Cited by 4 publications

References 86 publications

High‐Resolution Soil Moisture Data Reveal Complex Multi‐Scale Spatial Variability Across the United States

High‐Resolution Soil Moisture Data Reveal Complex Multi‐Scale Spatial Variability Across the United States

SMAP-HydroBlocks, a 30-m satellite-based soil moisture dataset for the conterminous US

Impacts of Sub‐Grid Topographic Representations on Surface Energy Balance and Boundary Conditions in the E3SM Land Model: A Case Study in Sierra Nevada

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