scholarly journals Spatially distributed water-balance and meteorological data from the Wolverton catchment, Sequoia National Park, California

2018 ◽  
Author(s):  
Roger C. Bales ◽  
Erin M. Stacy ◽  
Xiande Meng ◽  
Martha H. Conklin ◽  
Peter B. Kirchner ◽  
...  
Keyword(s):  
Water Balance ◽  
Sierra Nevada ◽  
Snow Depth ◽  
National Park ◽  
Meteorological Data ◽  
Soil Water Storage ◽  
Spatial And Temporal Patterns ◽  
Online Data ◽  
Data Repositories ◽  
Sequoia National Park

Abstract. Accurate water-balance measurements in the seasonal, snow-dominated Sierra Nevada are important for forest and downstream water management. However, few sites in the southern Sierra offer detailed records of the spatial and temporal patterns of snowpack and soil-water storage, and the fluxes affecting them, i.e. precipitation as rain and snow, snowmelt, evapotranspiration, and runoff. To explore these stores and fluxes we instrumented the Wolverton basin (2180–2750 m) in Sequoia National Park with distributed, continuous sensors. This 2006–2016 record of snow depth, soil moisture and soil temperature, and meteorological data quantifies the hydrologic inputs and storage in a mostly undeveloped catchment. Clustered sensors record lateral differences with regards to aspect and canopy cover at approximately 2250 and 2625 m in elevation, where two meteorological stations are installed. Meteorological stations record air temperature, relative humidity, radiation, precipitation, wind speed and direction, and snow depth. Data are available at hourly intervals by water year (1 October–30 September) in non-proprietary formats from online data repositories ( https://doi.org/10.6071/M3S94T).

scholarly journals Spatially distributed water-balance and meteorological data from the Wolverton catchment, Sequoia National Park, California

2018 ◽  
Vol 10 (4) ◽  
pp. 2115-2122
Author(s):  
Roger C. Bales ◽  
Erin M. Stacy ◽  
Xiande Meng ◽  
Martha H. Conklin ◽  
Peter B. Kirchner ◽  
...  
Keyword(s):  
Water Balance ◽  
Sierra Nevada ◽  
Snow Depth ◽  
National Park ◽  
Meteorological Data ◽  
Soil Water Storage ◽  
Spatial And Temporal Patterns ◽  
Online Data ◽  
Data Repositories ◽  
Sequoia National Park

Abstract. Accurate water-balance measurements in the seasonal, snow-dominated Sierra Nevada are important for forest and downstream water management. However, few sites in the southern Sierra offer detailed records of the spatial and temporal patterns of snowpack and soil-water storage and the fluxes affecting them, i.e., precipitation as rain and snow, snowmelt, evapotranspiration, and runoff. To explore these stores and fluxes we instrumented the Wolverton basin (2180–2750 m) in Sequoia National Park with distributed, continuous sensors. This 2006–2016 record of snow depth, soil moisture and soil temperature, and meteorological data quantifies the hydrologic inputs and storage in a mostly undeveloped catchment. Clustered sensors record lateral differences with regards to aspect and canopy cover at approximately 2250 and 2625 m in elevation, where two meteorological stations are installed. Meteorological stations record air temperature, relative humidity, radiation, precipitation, wind speed and direction, and snow depth. Data are available at hourly intervals by water year (1 October–30 September) in non-proprietary formats from online data repositories (https://doi.org/10.6071/M3S94T).

scholarly journals Spatially distributed water-balance and meteorological data from the rain–snow transition, southern Sierra Nevada, California

2018 ◽  
Vol 10 (4) ◽  
pp. 1795-1805 ◽  
Author(s):  
Roger Bales ◽  
Erin Stacy ◽  
Mohammad Safeeq ◽  
Xiande Meng ◽  
Matthew Meadows ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Water Balance ◽  
Sierra Nevada ◽  
Snow Depth ◽  
Meteorological Data ◽  
Critical Zone ◽  
Sensor Nodes ◽  
Critical Zone Observatory ◽  
Spatially Distributed ◽  
Experimental Watersheds

Abstract. We strategically placed spatially distributed sensors to provide representative measures of changes in snowpack and subsurface water storage, plus the fluxes affecting these stores, in a set of nested headwater catchments. The high temporal frequency and distributed coverage make the resulting data appropriate for process studies of snow accumulation and melt, infiltration, evapotranspiration, catchment water balance, (bio)geochemistry, and other critical-zone processes. We present 8 years of hourly snow-depth, soil-moisture, and soil-temperature data, as well as 14 years of quarter-hourly streamflow and meteorological data that detail water-balance processes at Providence Creek, the upper part of which is at the current 50 % rain versus snow transition of the southern Sierra Nevada, California. Providence Creek is the long-term study cooperatively run by the Southern Sierra Critical Zone Observatory (SSCZO) and the USDA Forest Service Pacific Southwest Research Station's Kings River Experimental Watersheds (KREW). The 4.6 km2 montane Providence Creek catchment spans the current lower rain–snow transition elevation of 1500–2100 m. Two meteorological stations bracket the high and low elevations of the catchment, measuring air temperature, relative humidity, solar radiation, precipitation, wind speed and direction, and snow depth, and at the higher station, snow water equivalent. Paired flumes at three subcatchments and a V-notch weir at the integrating catchment measure quarter-hourly streamflow. Measurements of meteorological and streamflow data began in 2002. Between 2008 and 2010, 50 sensor nodes were added to measure distributed snow depth, air temperature, soil temperature, and soil moisture within the top 1 m below the surface. These sensor nodes were installed to capture the lateral differences of aspect and canopy coverage. Data are available at hourly and daily intervals by water year (1 October–30 September) in nonproprietary formats from online data repositories. Data for the Southern Sierra Critical Zone Observatory distributed snow and soil datasets are at https://doi.org/10.6071/Z7WC73. Kings River Experimental Watersheds meteorological data are available from https://doi.org/10.2737/RDS-2018-0028 and stream-discharge data are available from https://doi.org/10.2737/RDS-2017-0037.

scholarly journals Spatially distributed water-balance and meteorological data from the rain-snow transition, southern Sierra Nevada, California

2018 ◽  
Author(s):  
Roger Bales ◽  
Erin Stacy ◽  
Mohammad Safeeq ◽  
Xiande Meng ◽  
Matthew Meadows ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Water Balance ◽  
Soil Temperature ◽  
Sierra Nevada ◽  
Air Temperature ◽  
Snow Depth ◽  
Meteorological Data ◽  
Sensor Nodes ◽  
Subsurface Water ◽  
Spatially Distributed

Abstract. We strategically placed spatially distributed sensors to provide representative measures of changes in snowpack and subsurface water storage, plus the fluxes affecting these stores, in a set of nested headwater catchments. We present eight years of hourly snow-depth, soil-moisture and soil-temperature data, and 14 years of quarter-hourly streamflow and meteorological data that detail water-balance processes at the rain-snow transition at Providence Creek in the southern Sierra Nevada, California. Providence Creek is the co-operated long-term study run by the Southern Sierra Critical Zone Observatory and the U.S.D.A. Forest Service Pacific Southwest Research Station's Kings River Experimental Watersheds. The 4-km2 montane Providence Creek catchment spans the current rain-snow transition elevation of 1500–2100 m. Two meteorological stations bracket the high and low elevations of the catchment, measuring air temperature, relative humidity, solar radiation, precipitation, wind speed and direction, and snow depth, and at the higher station, snow water equivalent. Paired flumes at three subcatchments and a V-notch weir at the integrating catchment measure quarter-hourly streamflow. Measurements of meteorological and streamflow data began in 2002. Between 2008 and 2010, 50 sensor nodes were added to measure distributed snow depth, air temperature, soil temperature and soil moisture down to a depth of 1 m below the surface. These sensor nodes were installed to capture the lateral differences of aspect and canopy coverage. Data are available at hourly and daily intervals by water year (October 1–September 30) in non-proprietary formats from online data repositories (https://doi.org/10.6071/Z7WC73 and https://doi.org/10.2737/RDS-2017-0037).

scholarly journals Seasonal influence of snow conditions on Dall’s sheep productivity in Wrangell-St Elias National Park and Preserve

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0244787
Author(s):  
Christopher L. Cosgrove ◽  
Jeff Wells ◽  
Anne W. Nolin ◽  
Judy Putera ◽  
Laura R. Prugh
Keyword(s):  
Snow Depth ◽  
National Park ◽  
Meteorological Data ◽  
Multiple Regression Model ◽  
Snow Accumulation ◽  
Population Declines ◽  
Climate Conditions ◽  
Remote Cameras ◽  
Snow Conditions ◽  
Dall’S Sheep

Dall’s sheep (Ovis dalli dalli) are endemic to alpine areas of sub-Arctic and Arctic northwest America and are an ungulate species of high economic and cultural importance. Populations have historically experienced large fluctuations in size, and studies have linked population declines to decreased productivity as a consequence of late-spring snow cover. However, it is not known how the seasonality of snow accumulation and characteristics such as depth and density may affect Dall’s sheep productivity. We examined relationships between snow and climate conditions and summer lamb production in Wrangell-St Elias National Park and Preserve, Alaska over a 37-year study period. To produce covariates pertaining to the quality of the snowpack, a spatially-explicit snow evolution model was forced with meteorological data from a gridded climate re-analysis from 1980 to 2017 and calibrated with ground-based snow surveys and validated by snow depth data from remote cameras. The best calibrated model produced an RMSE of 0.08 m (bias 0.06 m) for snow depth compared to the remote camera data. Observed lamb-to-ewe ratios from 19 summers of survey data were regressed against seasonally aggregated modelled snow and climate properties from the preceding snow season. We found that a multiple regression model of fall snow depth and fall air temperature explained 41% of the variance in lamb-to-ewe ratios (R2 = .41, F(2,38) = 14.89, p<0.001), with decreased lamb production following deep snow conditions and colder fall temperatures. Our results suggest the early establishment and persistence of challenging snow conditions is more important than snow conditions immediately prior to and during lambing. These findings may help wildlife managers to better anticipate Dall’s sheep recruitment dynamics.

A NEW CALIFORNIA EUPHYDRYAS (LEPID., RHOPALOCERA)

1929 ◽  
Vol 61 (2) ◽  
pp. 44-45
Author(s):  
J. D. Gunder
Keyword(s):  
New Mexico ◽  
Sierra Nevada ◽  
Great Basin ◽  
Rocky Mountains ◽  
National Park ◽  
Western Montana ◽  
Sequoia National Park ◽  
Parental Stock ◽  
Sierra Nevada Mountains

The chain of Rocky Mountains extending south from Canada through western Montana. Wyoming, Colorado and into northern New Mexico produce a series of butterflies which are at prespnt referable under an anicia-brucei classification. Various races from this supposed parental stock are found in southwestern Colorado, Utah, the Great Basin of Nevada and elsewhere with members of the clan branching down into New Mexico. For several years I have been hoping to find representatives of this group reaching over into the Sierra Nevada Mountains of California. In 1927 when on Alta Peak in Sequoia National Park, I took two males and in 1928 Mr. Walter Ireland captured four females in the same locality which I find to be closely related to the above mentioned breed.

scholarly journals On the effect of upwind emission controls on ozone in Sequoia National Park

2017 ◽  
Author(s):  
Claire E. Buysse ◽  
Jessica A. Munyan ◽  
Clara A. Bailey ◽  
Alexander Kotsakis ◽  
Jessica A. Sagona ◽  
...  
Keyword(s):  
Sierra Nevada ◽  
Regulatory Focus ◽  
National Park ◽  
Western Slope ◽  
Sequoia National Park ◽  
Time Period ◽  
Sierra Nevada Mountains ◽  
Emission Controls ◽  
The U.S ◽  
Do So

Abstract. Sequoia National Park (SNP) experiences the worst ozone (O3) pollution of any national park in the U.S. SNP is located on the western slope of the Sierra Nevada Mountains, downwind of the San Joaquin Valley (SJV), which is home to numerous cities ranked among the most O3-polluted in the U.S. Here, we investigate the influence of emission controls in the directly upwind SJV city of Visalia on O3 concentrations in SNP over a 12-yr time period (2001–2012). We show that export of nitrogen oxides (NOx) from the SJV plays a larger role in driving high O3 in SNP than does transport of O3. As a result, O3 in SNP has been more responsive to NOx emission reductions as a function of increasing downwind distance from the SJV. We report O3 trends by various concentration metrics, but do so separately for when environmental conditions are conducive to plant O3 uptake and for when high O3 is most common, which are time periods that occur at different times of day and year. We find that precursor emission controls have been less effective at reducing O3 concentrations in SNP in springtime, which is when plant O3 uptake in Sierra Nevada forests has been previously measured to be greatest. We discuss the implications of regulatory focus on high O3 days in SJV cities on O3 concentration trends and impacts in SNP.

scholarly journals On the effect of upwind emission controls on ozone in Sequoia National Park

2018 ◽  
Vol 18 (23) ◽  
pp. 17061-17076 ◽  
Author(s):  
Claire E. Buysse ◽  
Jessica A. Munyan ◽  
Clara A. Bailey ◽  
Alexander Kotsakis ◽  
Jessica A. Sagona ◽  
...  
Keyword(s):  
Sierra Nevada ◽  
Regulatory Focus ◽  
National Park ◽  
Western Slope ◽  
Ecosystem Impacts ◽  
Sequoia National Park ◽  
Time Period ◽  
Sierra Nevada Mountains ◽  
The Us ◽  
Emission Controls

Abstract. Ozone (O3) air pollution in Sequoia National Park (SNP) is among the worst of any national park in the US. SNP is located on the western slope of the Sierra Nevada Mountains downwind of the San Joaquin Valley (SJV), which is home to numerous cities ranked in the top 10 most O3-polluted in the US. Here, we investigate the influence of emission controls in the SJV on O3 concentrations in SNP over a 12-year time period (2001–2012). We show that the export of nitrogen oxides (NOx) from the SJV has played a larger role in driving high O3 in SNP than transport of O3. As a result, O3 in SNP has been more responsive to NOx emission reductions than in the upwind SJV city of Visalia, and O3 concentrations have declined faster at a higher-elevation monitoring station in SNP than at a low-elevation site nearer to the SJV. We report O3 trends by various concentration metrics but do so separately for when environmental conditions are conducive to plant O3 uptake and for when high O3 is most common, which are time periods that occur at different times of day and year. We find that precursor emission controls have been less effective at reducing O3 concentrations in SNP in springtime, which is when plant O3 uptake in Sierra Nevada forests has been previously measured to be greatest. We discuss the implications of regulatory focus on high O3 days in SJV cities for O3 concentration trends and ecosystem impacts in SNP.

scholarly journals Regression Models for Soil Water Storage Estimation Using the ESA CCI Satellite Soil Moisture Product: A Case Study in Northeast Portugal

Water ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 37
Author(s):  
Tomás de Figueiredo ◽  
Ana Caroline Royer ◽  
Felícia Fonseca ◽  
Fabiana Costa de Araújo Schütz ◽  
Zulimar Hernández
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Regression Models ◽  
Meteorological Data ◽  
Water Storage ◽  
Model Performance ◽  
Soil Water Balance ◽  
Soil Water Storage ◽  
The Relationship

The European Space Agency Climate Change Initiative Soil Moisture (ESA CCI SM) product provides soil moisture estimates from radar satellite data with a daily temporal resolution. Despite validation exercises with ground data that have been performed since the product’s launch, SM has not yet been consistently related to soil water storage, which is a key step for its application for prediction purposes. This study aimed to analyse the relationship between soil water storage (S), which was obtained from soil water balance computations with ground meteorological data, and soil moisture, which was obtained from radar data, as affected by soil water storage capacity (Smax). As a case study, a 14-year monthly series of soil water storage, produced via soil water balance computations using ground meteorological data from northeast Portugal and Smax from 25 mm to 150 mm, were matched with the corresponding monthly averaged SM product. Linear (I) and logistic (II) regression models relating S with SM were compared. Model performance (r2 in the 0.8–0.9 range) varied non-monotonically with Smax, with it being the highest at an Smax of 50 mm. The logistic model (II) performed better than the linear model (I) in the lower range of Smax. Improvements in model performance obtained with segregation of the data series in two subsets, representing soil water recharge and depletion phases throughout the year, outlined the hysteresis in the relationship between S and SM.

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