scholarly journals Enhancing Engagement of Citizen Scientists to Monitor Precipitation Phase

2021 ◽  
Vol 9 ◽  
Author(s):  
Monica M. Arienzo ◽  
Meghan Collins ◽  
Keith S. Jennings

Recent literature has highlighted how citizen science approaches can engage volunteers, expand scientific literacy, and accomplish targeted research objectives. However, there is limited information on how specific recruitment, retention, and engagement strategies enhance scientific outcomes. To help fill this important information gap, we detail the use of various approaches to engage citizen scientists in the collection of precipitation phase data (rain, snow, or mixed). In our study region, the Sierra Nevada and Central Basin and Range of California and Nevada near Lake Tahoe, a marked amount of annual precipitation falls near freezing. At these air temperatures, weather forecasts, land surface models, and satellites all have difficulty correctly predicting and observing precipitation phase, making visual observations the most accurate approach. From January to May 2020, citizen scientists submitted timestamped, geotagged observations of precipitation phase through the Citizen Science Tahoe mobile phone application. Our recruitment strategy included messaging to winter, weather, and outdoor enthusiasts combined with amplification through regional groups, which resulted in over 199 citizen scientists making 1,003 ground-based observations of rain, snow, and mixed precipitation. We enhanced engagement and retention by targeting specific storms in the region through text message alerts that also allowed for questions, clarifications, and training opportunities. We saw a high retention rate (88%) and a marked increase in the number of observations following alerts. For quality control of the data, we combined various meteorological datasets and compared to the citizen science observations. We found that 96.5% of submitted data passed our quality control protocol, which enabled us to evaluate rain-snow partitioning patterns. Snow was the dominant form of precipitation at air temperatures below and slightly above freezing, with both ecoregions expressing a 50% rain-snow air temperature threshold of 4.2°C, a warmer value than what would be incorporated into most land surface models. Thus, the use of a lower air temperature threshold in these areas would produce inaccuracies in event-based rain-snow proportions. Overall, our high retention rate, data quality, and rain-snow analysis were supported by the recruitment strategy, text message communication, and simplicity of the survey design. We suggest other citizen science projects may follow the approaches detailed herein to achieve their scientific objectives.

2016 ◽  
Author(s):  
Wenli Wang ◽  
Annette Rinke ◽  
John C. Moore ◽  
Duoying Ji ◽  
Xuefeng Cui ◽  
...  

Abstract. A realistic simulation of snow cover and its thermal properties are important for accurate modelling of permafrost. We analyze simulated relationships between air and near-surface (20 cm) soil temperatures in the Northern Hemisphere permafrost region during winter, with a particular focus on snow insulation effects in nine land surface models and compare them with observations from 268 Russian stations. There are large across-model differences as expressed by simulated differences between near-surface soil and air temperatures, (ΔT), of 3 to 14 K, in the gradients between soil and air temperatures (0.13 to 0.96 °C/°C), and in the relationship between ΔT and snow depth. The observed relationship between ΔT and snow depth can be used as a metric to evaluate the effects of each model's representation of snow insulation, and hence guide improvements to the model’s conceptual structure and process parameterizations. Models with better performance apply multi-layer snow schemes and consider complex snow processes. Some models show poor performance in representing snow insulation due to underestimation of snow depth and/or overestimation of snow conductivity. Generally, models identified as most acceptable with respect to snow insulation simulate reasonable areas of near-surface permafrost (12–16 million km2). However, there is not a simple relationship between the quality of the snow insulation in the acceptable models and the simulated area of Northern Hemisphere near-surface permafrost, likely because several other factors such as differences in the treatment of soil organic matter, soil hydrology, surface energy calculations, and vegetation also provide important controls on simulated permafrost distribution.


2021 ◽  
Author(s):  
Sandy P. Harrison ◽  
Wolfgang Cramer ◽  
Oskar Franklin ◽  
Iain Colin Prentice ◽  
Han Wang ◽  
...  

2006 ◽  
Vol 87 (10) ◽  
pp. 1367-1380 ◽  
Author(s):  
A. J. Dolman ◽  
J. Noilhan ◽  
P. Durand ◽  
C. Sarrat ◽  
A. Brut ◽  
...  

The Second Global Soil Wetness Project (GSWP-2) is an initiative to compare and evaluate 10-year simulations by a broad range of land surface models under controlled conditions. A major product of GSWP-2 is the first global gridded multimodel analysis of land surface state variables and fluxes for use by meteorologists, hydrologists, engineers, biogeochemists, agronomists, botanists, ecologists, geographers, climatologists, and educators. Simulations by 13 land models from five nations have gone into production of the analysis. The models are driven by forcing data derived from a combination of gridded atmospheric reanalyses and observations. The resulting analysis consists of multimodel means and standard deviations on the monthly time scale, including profiles of soil moisture and temperature at six levels, as well as daily and climatological (mean annual cycle) fields for over 50 land surface variables. The monthly standard deviations provide a measure of model agreement that may be used as a quality metric. An overview of key characteristics of the analysis is presented here, along with information on obtaining the data.


2012 ◽  
Vol 16 (9) ◽  
pp. 3451-3460 ◽  
Author(s):  
W. T. Crow ◽  
S. V. Kumar ◽  
J. D. Bolten

Abstract. The lagged rank cross-correlation between model-derived root-zone soil moisture estimates and remotely sensed vegetation indices (VI) is examined between January 2000 and December 2010 to quantify the skill of various soil moisture models for agricultural drought monitoring. Examined modeling strategies range from a simple antecedent precipitation index to the application of modern land surface models (LSMs) based on complex water and energy balance formulations. A quasi-global evaluation of lagged VI/soil moisture cross-correlation suggests, when globally averaged across the entire annual cycle, soil moisture estimates obtained from complex LSMs provide little added skill (< 5% in relative terms) in anticipating variations in vegetation condition relative to a simplified water accounting procedure based solely on observed precipitation. However, larger amounts of added skill (5–15% in relative terms) can be identified when focusing exclusively on the extra-tropical growing season and/or utilizing soil moisture values acquired by averaging across a multi-model ensemble.


2017 ◽  
Vol 18 (3) ◽  
pp. 897-915 ◽  
Author(s):  
Jennifer L. Jefferson ◽  
Reed M. Maxwell ◽  
Paul G. Constantine

Abstract Land surface models, like the Common Land Model component of the ParFlow integrated hydrologic model (PF-CLM), are used to estimate transpiration from vegetated surfaces. Transpiration rates quantify how much water moves from the subsurface through the plant and into the atmosphere. This rate is controlled by the stomatal resistance term in land surface models. The Ball–Berry stomatal resistance parameterization relies, in part, on the rate of photosynthesis, and together these equations require the specification of 20 input parameters. Here, the active subspace method is applied to 2100 year-long PF-CLM simulations, forced by atmospheric data from California, Colorado, and Oklahoma, to identify which input parameters are important and how they relate to three quantities of interest: transpiration, stomatal resistance from the sunlit portion of the canopy, and stomatal resistance from the shaded portion. The slope (mp) and intercept (bp) parameters associated with the Ball–Berry parameterization are consistently important for all locations, along with five parameters associated with ribulose bisphosphate carboxylase/oxygenase (RuBisCO)- and light-limited rates of photosynthesis [CO2 Michaelis–Menten constant at 25°C (kc25), maximum ratio of oxygenation to carboxylation (ocr), quantum efficiency at 25°C (qe25), maximum rate of carboxylation at 25°C (vcmx25), and multiplier in the denominator of the equation used to compute the light-limited rate of photosynthesis (wj1)]. The importance of these input parameters, quantified by the active variable weight, and the relationship between the input parameters and quantities of interest vary seasonally and diurnally. Input parameter values influence transpiration rates most during midday, summertime hours when fluxes are large. This research informs model users about which photosynthesis and stomatal resistance parameters should be more carefully selected. Quantifying sensitivities associated with the stomatal resistance term is necessary to better understand transpiration estimates from land surface models.


2018 ◽  
Vol 10 (5) ◽  
pp. 751 ◽  
Author(s):  
Sujay Kumar ◽  
Thomas Holmes ◽  
David Mocko ◽  
Shugong Wang ◽  
Christa Peters-Lidard

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