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17.11: Canopy Flows - Geosciences


The leaf or needle layer of a crop or forest is called a canopy. The average winds in the air space between these plant-canopy or forest-canopy obstacles is the canopy flow.

Just above the top of the canopy, the flow is approximately logarithmic with height (Fig. 17.40a). For statically neutral conditions in the surface layer:

( egin{align}
&M=frac{u_{*}}{k} ln left(frac{z-d}{z_{o}-d} ight)
& ext { for } z geq h_{c}
ag{17.53}end{align})

where M is wind speed, z is height above ground, u* is the friction velocity (a measure of the drag force per unit surface area of the ground), k ≈ 0.4 is the von Kármán constant, d is the displacement distance (0 ≤ d ≤ hc), and zo is the roughness length, for an average canopy-top height of hc.

If you can measure the actual wind speed M at 3 or more heights z within 20 m above the top of the canopy, then you can use the following procedure to find d, zo, and u*: (1) use a spreadsheet to plot your M values on a linear horizontal axis vs. their [z–d] values on a logarithmic vertical axis; (2) experiment with different values of d until you find the one that aligns your wind points into a straight line; (3) extrapolate that straight line to M = 0, and note the resulting intercept on the vertical axis, which gives the roughness length zo. Finally, (4)pick any point exactly on the plotted line, and then plug in its M and z values, along with the d and zo values just found, to calculate u* using eq. (17.53).

If you do not have measurements of wind speed above the canopy top, you can use the following crude approximations to estimate the needed parameters: d ≈ 0.65·hc and zo ≈ 0.1·hc. Methods to estimate u* are given in the Boundary-Layer chapter.

The average wind speed at the average canopy-top height is Mc. For the crude approximations above, we find that Mc ≈ 3.13 u*.

Within the top 3/4 of canopy, an exponential formula describes the average wind-speed M profile:

( egin{align} M=M_{c} cdot exp left[a cdotleft(frac{z}{h_{c}}-1 ight) ight] quad ext { for } 0.5 h_{c} leq z leq h_{c} ag{17.54}end{align})

where a is an attenuation coefficient that increases with increasing leaf area and decreases as the mean distance between individual plants increases. Typical values are: a ≈ 2.5 - 2.8 for oats and wheat; 2.0 - 2.7 for mature corn; 1.3 for sunflowers; 1.0 - 1.1 for larch and small evergreen trees; and 0.4 for a citrus orchard. The exponential and log-wind speeds match at the average canopy top hc.

For a forest with relatively open trunk space (i.e., only the tree trunks without many leaves, branches, or smaller underbrush), the previous equation fails. Instead, a weak relative maximum wind speed can occur (Fig. 17.40b). In such forests, if the canopy is very dense, then the sub-canopy (trunk space) flow can be relatively disconnected from the flow above the tree tops. Weak katabatic flows can exist in the trunk space day and night.

Sample Application (§)

Given these wind measurements over a 2 m high corn crop: [z (m), M (m s–1)] = [5, 3.87] , [10, 5.0] , [20, 6.01]. Find the displacement distance, roughness length, and friction velocity. If the attenuation coefficient is 2.5, plot wind speed M vs. height over 0.5 m ≤ z ≤ 5 m.

Find the Answer

Given: hc =2m, [z (m), M (m s–1)] listed above, a = 2.5.

Find: d = ? m, zo = ? m, u* = ? m s–1, and plot M vs. z.

Guess d = 0, and plot M vs. log(z–d) on a spreadsheet. This d is too small (see graph below), because the curve is concave up. Guess d = 4, which is too large, because curve is concave down. After other guesses (some not shown), I find that d = 1.3 m gives the straightest line.

Next, extrapolate on the semi-log graph to M = 0, which gives an intercept of zo = 0.2 m.

Solve eq. (17.53) for u* = k·M/ln[(z–d)/zo]

u* = 0.4(5m s–1)/ln[(10–1.3)/0.2] = 0.53 ms–1

Solve eq. (17.53) for Mc at z = hc = 2 m:

Mc = [(0.53m s–1)/0.4] · ln[(2–1.3m)/0.2] = 1.66 m s–1

Use eq. (17.54) to find M for a range of heights below hc, and use eq. (17.53) for heights above hc:

Check: Shape of curve looks reasonable.

Exposition: For this exercise, zo = 0.1 hc, and d = 0.65 hc. Namely, the crude approximations are OK.

The collection of buildings and trees that make up a city is sometimes called an urban canopy. These obstacles cause an average canopy-flow wind similar to that for forests and crops (Fig. 17.40a).

However, winds at any one location in the city can be quite different. For example, the street corridors between tall buildings can channel flow similar to the flow in narrow valleys. Hence these corridors are sometimes called urban canyons. Also, taller buildings can deflect down to the surface some of the faster winds aloft. This causes much greater wind speeds and gusts near the base of tall buildings than near the base of shorter buildings.

Cities can be 2 - 12°C degrees warmer than the surrounding rural countryside — an effect called the urban heat island (UHI, Fig. 17.41). Reasons include the abundance of concrete, glass and asphalt, which capture and store the solar heat during daytime and reduce the IR cooling at night. Also, vegetated areas are reduced in cities, and rainwater is channeled away through storm drains. Hence, there is less evaporative cooling. Also, fuel and electrical consumption by city residents adds heat via heating, air conditioning, industry, and transportation.

The city–rural temperature difference ∆TUHI is greatest during clear calm nights, because the city stays warm while rural areas cool considerably due to IR radiation to space. The largest values ∆TUHI_ max occur near the city center (Fig. 17.42), at the location of greatest density of high buildings and narrow streets. For clear, calm nights, this relationship is described by

( egin{align} Delta T_{U H I_{-} max} approx a+b cdot ln (H / W) ag{17.55}end{align})

where a = 7.54°C and b = 3.97°C. H is the average height (m) of the buildings in the downtown city core, W is the average width (m) of the streets at the same location, and H/W is dimensionless.

Temperature difference is much smaller during daytime. When averaged over a year (including windy and cloudy periods of minimal UHI), the average ∆TUHI at the city center is only 1 to 2°C.

During periods of fair weather and light synopticscale winds, the warm city can generate circulations similar to sea breezes, with inflow of low-altitude rural air toward the city, and rising air over the hottest parts of town. These circulations can enhance clouds, and trigger or strengthen thunderstorms over and downwind of the city. With light to moderate winds, the UHI area is asymmetric, extending much further from the city in the downwind direction (Fig. 17.41), and the effluent (heat, air pollution, odors) from the city can be observed downwind as an urban plume (Fig. 17.43).


Arboreal Epiphytes in the Soil-Atmosphere Interface: How Often Are the Biggest “Buckets” in the Canopy Empty?

1/3 of their time in the dry state (0&ndash10% of water storage capacity). Even data from Costa Rican cloud forest sites found the epiphyte community was saturated only 1/3 of the time and that internal leaf water storage was temporally dynamic enough to aid in precipitation interception. Analysis of the epi-soils associated with epiphytes further revealed the extent to which the epiphyte bucket emptied&mdashas even the canopy soils were often <50% saturated (29&ndash53% of all days observed). Results clearly show that the epiphyte bucket is more dynamic than currently assumed, meriting further research on epiphyte roles in precipitation interception, redistribution to the surface and chemical composition of &ldquonet&rdquo precipitation waters reaching the surface.


Landscape composition or configuration: which contributes more to catchment hydrological flows and variations?

Landscape composition and configuration determine the generation and exchange of water flows among different landscape patches and may affect catchment hydrological flows and variations. However, the poor understanding on the effects of landscape patterns on hydrological processes limits the implementation of landscape planning and management practices for regulating catchment water resources at the practical level.

Objectives

The aims are to determine the relationship between the landscape pattern and hydrological flows and variations, and to compare the contributions of landscape composition and configuration to the hydrological flows and variations.

Methods

Landscape patterns were quantified and hydrological flows and variations were observed in a Chinese subtropical catchment with ten sub-catchments during 2011–2017. The relationship among landscape composition, configuration, and hydrological flows and variations was analysed by Pearson correlation, and their relative contributions were determined by the variance partitioning analysis.

Results

Landscape composition and configuration were significantly correlated with catchment stream, direct, and base flows and variations, of which high fragmentation degree increased stream and base flows, great shape complexity aggravated the volatility of stream flow, high patch aggregation adversely impacted stream, direct, and base flows, and high patch evenness and lower richness increased stream and base flows. Landscape composition and configuration indices could be employed to effectively predict hydrological flows and variations.

Conclusions

Landscape configuration had greater contribution than landscape composition to subtropical catchment hydrological flows and variations, and optimizing landscape configuration could improve our regulatory capacity and ability of catchment water resources management and utilization in the subtropical catchments at the practical level.


Modelling tree stem-water dynamics over an Amazonian rainforest

Binyan Yan, Jackson School of Geosciences, the University of Texas at Austin, Austin, TX 78712.

Jiafu Mao, Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN 37831-6301.

Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN

Binyan Yan, Jackson School of Geosciences, the University of Texas at Austin, Austin, TX 78712.

Jiafu Mao, Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN 37831-6301.

Jackson School of Geosciences, the University of Texas at Austin, Austin, TX

Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN

Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN

Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN

Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN

Computational Sciences and Engineering Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN

Jackson School of Geosciences, the University of Texas at Austin, Austin, TX

Binyan Yan, Jackson School of Geosciences, the University of Texas at Austin, Austin, TX 78712.

Jiafu Mao, Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN 37831-6301.

Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN

Binyan Yan, Jackson School of Geosciences, the University of Texas at Austin, Austin, TX 78712.

Jiafu Mao, Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN 37831-6301.

Jackson School of Geosciences, the University of Texas at Austin, Austin, TX

Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN

Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN

Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN

Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN

Computational Sciences and Engineering Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN

Abstract

A novel tree stem-water model was developed to capture the dynamics of stem-water storage and its contribution to daily transpiration. The module was incorporated into the Community Land Model (CLM), where it was used to test model sensitivity to stem-water content for an evergreen rainforest site in Amazonia, that is, the BR-Sa3 eddy covariance site. With the inclusion of the stem-water storage, CLM produced greater dry-season latent heat flux that was closer to observations, facilitated by easier canopy access to a nearby stem-water source, rather than solely dependent on soil water. The simulated stem-water content also showed seasonal variations in magnitude, along with the seasonal variations in sap flow rate. Stored stem-water of a single mature tree was estimated to contribute 20–80 kg/day of water to transpiration during the wet season and 90–110 kg/day during the dry season, thereby partially replacing soil water and maintaining plant transpiration during the dry season. Diurnally, stem-water content declined as water was extracted for transpiration in the morning and then was refilled from soil water beginning in the afternoon and through the night. The dynamic discharge and recharge of stem storage was also shown to be regulated by multiple environmental drivers. Our study indicates that the inclusion of stem capacitance in CLM significantly improves model simulations of dry-season water and heat fluxes, in terms of both magnitude and timing.

Figure S1. Peak values of latent heat flux on daily basis for the three months in the 2002 dry season. The bottom and top of the box represent lower and upper quartiles (25th and 75th percentile), central bar indicates the median. The two ends of whiskers indicate 95% confident interval.

Figure S2. The same as Figure S1 but for sensible heat flux.

Figure S3. The onset and magnitude of the hysteresis between basal and canopy sap flows in September 2002. Values in the figure are monthly mean.

Figure S4. The same as Figure S3 but for October 2002.

Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.


17.11: Canopy Flows - Geosciences

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Abstract

This paper presents results from experiments in a large flume on wave and flow attenuation by a full-scale artificial Posidonia oceanica seagrass meadow in shallow water. Wave height and in-canopy wave-induced flows were reduced by the meadow under all tested regular and irregular wave conditions, and were affected by seagrass density, submergence and distance from the leading edge. The energy of irregular waves was reduced at all components of the spectra, but reduction was greater at the peak spectral frequency. Energy dissipation factors were largest for waves with small orbital amplitudes and at low wave Reynolds numbers. An empirical model, commonly applied to predict friction factors by rough beds, proved applicable to the P. oceanica bed. However at the lowest Reynolds numbers, under irregular waves, the data deviated significantly from the model. In addition, the wave-induced flow dissipation in the lower canopy increased with increasing wave orbital amplitude and increasing density of the mimics. The analysis of the wave-induced flow spectra confirm this trend: the reduction of flow was greatest at the longer period component of the spectra. Finally, we discuss the implications of these findings for sediment dynamics and the role of P. oceanica beds in protecting the shore from erosion.

Highlights

► Full scale tests with Posidonia oceanica indicate that the seagrass reduces wave energy and wave-induced flows. ► Energy dissipation factors produced by the submerged canopy decay with wave orbital amplitude. ► Energy dissipation may be predicted by existing empirical formulae and canopy roughness may be estimated. ► In-canopy wave-induced flow reduction increases with increasing wave orbital amplitude and with increasing period of the flow spectra component. ► Effects of plant density, submergence ratios (hs/D) and distance from the leading edge were analysed.


Low-level jets and above-canopy drainage as causes of turbulent exchange in the nocturnal boundary layer

Abstract. Sodar (SOund Detection And Ranging), eddy-covariance, and tower profile measurements of wind speed and carbon dioxide were performed during 17 consecutive nights in complex terrain in northern Taiwan. The scope of the study was to identify the causes for intermittent turbulence events and to analyze their importance in nocturnal atmosphere–biosphere exchange as quantified with eddy-covariance measurements. If intermittency occurs frequently at a measurement site, then this process needs to be quantified in order to achieve reliable values for ecosystem characteristics such as net ecosystem exchange or net primary production.

Fourteen events of intermittent turbulence were identified and classified into above-canopy drainage flows (ACDFs) and low-level jets (LLJs) according to the height of the wind speed maximum. Intermittent turbulence periods lasted between 30 and 110 min. Towards the end of LLJ or ACDF events, positive vertical wind velocities and, in some cases, upslope flows occurred, counteracting the general flow regime at nighttime. The observations suggest that the LLJs and ACDFs penetrate deep into the cold air pool in the valley, where they experience strong buoyancy due to density differences, resulting in either upslope flows or upward vertical winds.

Turbulence was found to be stronger and better developed during LLJs and ACDFs, with eddy-covariance data presenting higher quality. This was particularly indicated by spectral analysis of the vertical wind velocity and the steady-state test for the time series of the vertical wind velocity in combination with the horizontal wind component, the temperature, and carbon dioxide.

Significantly higher fluxes of sensible heat, latent heat, and shear stress occurred during these periods. During LLJs and ACDFs, fluxes of sensible heat, latent heat, and CO2 were mostly one-directional. For example, exclusively negative sensible heat fluxes occurred while intermittent turbulence was present. Latent heat fluxes were mostly positive during LLJs and ACDFs, with a median value of 34 W m −2 , while outside these periods the median was 2 W m −2 . In conclusion, intermittent turbulence periods exhibit a strong impact on nocturnal energy and mass fluxes.


Monoslope roof wind load

RE: Monoslope roof wind load

RE: Monoslope roof wind load

The commentary states that for open building roofs with a top and bottom surface, the pressure coefficient should be separated for the effect of top and bottom pressures, or conservatively, each surface could be designed using the full pressure coefficient. I don't know of any published guidance on how to separate the pressures, but here is how I have been doing it:

1. Calculate the wind pressure using the "Clear Wind Flow" pressure coefficient. This represents the net pressure acting on the top and bottom surface.
2. Calculate the wind pressure using the "Obstructed Wind Flow" pressure coefficient. The code says that obstructed wind flow denotes objects below roof inhibiting wind flow (>50% blockage). Since this could be applied to a condition with 100% blockage, I interpret this pressure to be acting on the top surface only.
3. The difference between the pressures calculated in steps 1 and 2 is the pressure acting on the bottom surface.

Each surface should be also be checked using the +/- 16 psf code minimum. If the canopy is adjacent to a building, I would apply the wall pressure to the bottom surface of the canopy for some edge distance based on judgement.

RE: Monoslope roof wind load

RE: Monoslope roof wind load

RE: Monoslope roof wind load

RE: Monoslope roof wind load

I received some help from ASCE on my question.
“Based on the question, it seems that the designer is only asking about the soffit which suggests that he has been able to calculate roof load on the top surface. While there may be some ground effects that will reduce the flow separation under the bottom surface of the structure relative to the flow over the top surface, the clearance is enough in our opinion that he should design the soffit for the same wind loads as the roof surface.”

The following disclaimer was added to their response:
"Please note this information is the personal opinion of the subcommittee members that reviewed your question and is not an interpretation of the ASCE 7-10 standard."


On flows in simulated urban canopies

Flow and turbulence within building canopies continue to be a topic of profound interest in the context of pedestrian comfort, wind loading, contaminant dispersion and energy usage in populated urban areas. Many experimental studies have been reported on this topic, but they either deal with wind/water tunnel measurements (at low Reynolds numbers) or complex urban building clusters (where the results are site dependent and difficult to interpret). To avert such problems, an instrumented mock building cluster made of a regular array of man-sized objects (shipping containers) placed in the atmospheric boundary layer was used to investigate spatial flow adjustment, flow patterns (as a function of approach angle) and turbulence within the building canopy. A new scaling is proposed for the characteristic canopy velocity based on the approach flow and canopy morphology, which was found to perform well when evaluated against experimental data. The flow adjustment at the leading and trailing edges of the canopy was found to be in good agreement with the formulation of Belcher et al. (J Fluid Mech 488:369–398, 2003). The results have applications to developing simple and fast contaminant transport and dispersion models that can be used in conjunction with emergency response.

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