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1.
Int J Sports Physiol Perform ; 17(8): 1170-1178, 2022 Aug 01.
Article in English | MEDLINE | ID: mdl-35537708

ABSTRACT

PURPOSE: To investigate the relationship between pacing strategy and performance during uphill and downhill running-specifically, what distribution of energy corresponds to faster race finish times between and among participants. METHODS: Eighteen years of race data from a 10.2-mile running race with an uphill first half and a downhill second half were analyzed to identify relationships between pacing and performance. A pacing coefficient (PC), equal to a participant's ascent time divided by finishing time (FT), was used to define each participant's pacing strategy. The American College of Sports Medicine metabolic running equation was used to estimate energy expenditure during the ascent, descent, and total race. Statistical analyses compared participants' PC to their FT and finishing place within their age and gender category. Additionally, FT and finishing place were compared between groups of participants who exhibited similar pacing strategies. RESULTS: PCs were positively associated with faster FTs (r2 = .120, P < .001) and better finishing positions (r2 = .104, P < .001). PCs above .600 were associated with the fastest average FTs and best average finishing position within age and gender categories (all P ≤ .047). CONCLUSIONS: Participants performed the best when energy expenditure increased no more than 10.4% during the uphill portion compared to their overall average. It is not possible to state that overly aggressive uphill efforts resulted in premature fatigue and thus slower decent times and worse race performance. However, participants should still avoid overly aggressive uphill pacing, as performance was associated with larger PCs.


Subject(s)
Running , Energy Metabolism , Fatigue , Humans
2.
Proc Natl Acad Sci U S A ; 117(10): 5190-5195, 2020 03 10.
Article in English | MEDLINE | ID: mdl-32094189

ABSTRACT

Climate change and population growth have increased demand for water in arid regions. For over half a century, cloud seeding has been evaluated as a technology to increase water supply; statistical approaches have compared seeded to nonseeded events through precipitation gauge analyses. Here, a physically based approach to quantify snowfall from cloud seeding in mountain cloud systems is presented. Areas of precipitation unambiguously attributed to cloud seeding are isolated from natural precipitation (<1 mm h-1). Spatial and temporal evolution of precipitation generated by cloud seeding is then quantified using radar observations and snow gauge measurements. This study uses the approach of combining radar technology and precipitation gauge measurements to quantify the spatial and temporal evolution of snowfall generated from glaciogenic cloud seeding of winter mountain cloud systems and its spatial and temporal evolution. The results represent a critical step toward quantifying cloud seeding impact. For the cases presented, precipitation gauges measured increases between 0.05 and 0.3 mm as precipitation generated by cloud seeding passed over the instruments. The total amount of water generated by cloud seeding ranged from 1.2 × 105 m3 (100 ac ft) for 20 min of cloud seeding, 2.4 × 105 m3 (196 ac ft) for 86 min of seeding to 3.4 x 105 m3 (275 ac ft) for 24 min of cloud seeding.

3.
Proc Natl Acad Sci U S A ; 115(6): 1168-1173, 2018 02 06.
Article in English | MEDLINE | ID: mdl-29358387

ABSTRACT

Throughout the western United States and other semiarid mountainous regions across the globe, water supplies are fed primarily through the melting of snowpack. Growing populations place higher demands on water, while warmer winters and earlier springs reduce its supply. Water managers are tantalized by the prospect of cloud seeding as a way to increase winter snowfall, thereby shifting the balance between water supply and demand. Little direct scientific evidence exists that confirms even the basic physical hypothesis upon which cloud seeding relies. The intent of glaciogenic seeding of orographic clouds is to introduce aerosol into a cloud to alter the natural development of cloud particles and enhance wintertime precipitation in a targeted region. The hypothesized chain of events begins with the introduction of silver iodide aerosol into cloud regions containing supercooled liquid water, leading to the nucleation of ice crystals, followed by ice particle growth to sizes sufficiently large such that snow falls to the ground. Despite numerous experiments spanning several decades, no direct observations of this process exist. Here, measurements from radars and aircraft-mounted cloud physics probes are presented that together show the initiation, growth, and fallout to the mountain surface of ice crystals resulting from glaciogenic seeding. These data, by themselves, do not address the question of cloud seeding efficacy, but rather form a critical set of observations necessary for such investigations. These observations are unambiguous and provide details of the physical chain of events following the introduction of glaciogenic cloud seeding aerosol into supercooled liquid orographic clouds.

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