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 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 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 31 years of hourly spatially distributed air temperature, humidity, and precipitation amount and phase from Reynolds Critical Zone Observatory

2018 ◽  
Vol 10 (2) ◽  
pp. 1197-1205 ◽  
Author(s):  
Patrick R. Kormos ◽  
Danny G. Marks ◽  
Mark S. Seyfried ◽  
Scott C. Havens ◽  
Andrew Hedrick ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Air Temperature ◽  
Precipitation Amount ◽  
Critical Zone ◽  
Dew Point ◽  
Elevation Gradients ◽  
Wide Range ◽  
Critical Zone Observatory ◽  
Reynolds Creek ◽  
Spatially Distributed

Abstract. Thirty-one years of spatially distributed air temperature, relative humidity, dew point temperature, precipitation amount, and precipitation phase data are presented for the Reynolds Creek Experimental Watershed, which is part of the Critical Zone Observatory network. The air temperature, relative humidity, and precipitation amount data are spatially distributed over a 10 m lidar-derived digital elevation model at an hourly time step using a detrended kriging algorithm. This 21 TB dataset covers a wide range of weather extremes in a mesoscale basin (238 km2) that encompasses the rain–snow transition zone and should find widespread application in earth science modeling communities. Spatial data allow for a more holistic analysis of basin means and elevation gradients, compared to weather station data measured at specific locations. Files are stored in the NetCDF file format, which allows for easy spatiotemporal averaging and/or subsetting. Data are made publicly available through an OPeNDAP-enabled THREDDS server hosted by Boise State University Libraries in support of the Reynolds Creek Critical Zone Observatory (https://doi.org/10.18122/B2B59V).

scholarly journals Inferring Soil Moisture Memory from Streamflow Observations Using a Simple Water Balance Model

2013 ◽  
Vol 14 (6) ◽  
pp. 1773-1790 ◽  
Author(s):  
Rene Orth ◽  
Randal D. Koster ◽  
Sonia I. Seneviratne
Keyword(s):  
Soil Moisture ◽  
Water Balance ◽  
Weather Forecasting ◽  
Water Balance Model ◽  
Balance Model ◽  
Catchment Scale ◽  
Spatially Distributed ◽  
Optimized Model ◽  
Streamflow Data

Abstract Soil moisture is known for its integrative behavior and resulting memory characteristics. Soil moisture anomalies can persist for weeks or even months into the future, making initial soil moisture a potentially important contributor to skill in weather forecasting. A major difficulty when investigating soil moisture and its memory using observations is the sparse availability of long-term measurements and their limited spatial representativeness. In contrast, there is an abundance of long-term streamflow measurements for catchments of various sizes across the world. The authors investigate in this study whether such streamflow measurements can be used to infer and characterize soil moisture memory in respective catchments. Their approach uses a simple water balance model in which evapotranspiration and runoff ratios are expressed as simple functions of soil moisture; optimized functions for the model are determined using streamflow observations, and the optimized model in turn provides information on soil moisture memory on the catchment scale. The validity of the approach is demonstrated with data from three heavily monitored catchments. The approach is then applied to streamflow data in several small catchments across Switzerland to obtain a spatially distributed description of soil moisture memory and to show how memory varies, for example, with altitude and topography.

Validation of SMAP and AMSR2 satellite soil moisture data over the Critical Zone Observatory in central Ganga plains, North India using ground-based observations

2021 ◽  
Author(s):  
Saroj Dash ◽  
Rajiv Sinha
Keyword(s):  
Soil Moisture ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Critical Zone ◽  
North India ◽  
Mean Square ◽  
Critical Zone Observatory ◽  
Level 3

<p>Soil moisture (SM) products derived from the passive satellite missions have been extensively used in various hydrological and environmental processes. However, validation of the satellite derived product is crucial for its reliability in several applications. In this study, we present a comprehensive validation of the descending SM product from Soil Moisture Active Passive (SMAP) Enhanced Level-3 (L3) radiometer (SMAP L3-Version 3) and the Advanced Microwave Scanning Radiometer 2 (AMSR2) Level-3 (Version 1), over the newly established Critical Zone Observatory (CZO) within the Ganga basin, North India. The AMSR2 soil moisture product used here, has been derived using the Land Parameter Retrieval Model (LPRM) algorithm. Four SM derived products from SMAP (L-band) and AMSR2 (C1- and C2- and X-band) are validated against the in-situ observations collected from 21 SM monitoring locations distributed over the CZO within a period from September 2017 to December 2019, for a total of 62 days. Since the remotely sensed SM product has a coarser spatial resolution (here 9 km for SMAP and 10 km for AMSR2), the assessment has been carried out for the temporal variation of the measured values. Four statistical metrics such as bias, root mean square error (RMSE), unbiased root-mean-square error (ubRMSE) and the correlation coefficient (R) have been used here for the evaluation. The SMAP Level-3 products are found to show a satisfactory correlation (R>0.6) compared to the other three SM product. Both the SMAP L3 and the AMSR2 C2 SM shows a negative bias, -0.05 m<sup>3</sup>/m<sup>3</sup> and -0.04 m<sup>3</sup>/m<sup>3 </sup>respectively whereas these values are found to be 0.04 m<sup>3</sup>/m<sup>3</sup> and 0.06 m<sup>3</sup>/m<sup>3</sup> for C1 and X bands of AMSR2, respectively. Furthermore, the RMSE between the SMAP L3 and in-situ data is 0.07 m<sup>3</sup>/m<sup>3</sup>, which is slightly underperformed when considering the required accuracy of SMAP. This is possibly due to variation in the sampling depth along with the sampling day distribution over CZO. The AMSR2 SM products (C1-, C2- and X-bands) are found to have a higher RMSE than SMAP L3, ranging from 0.08-0.1 m<sup>3</sup>/m<sup>3</sup>. In addition, the ubRMSE for all remotely sensed soil moisture product range from 0.06-0.08 m<sup>3</sup>/m<sup>3</sup> with the lowest value for the SMAP L3 and AMSR2 C1. The results in this study can be used further for relevant hydrological modelling along with evaluating various downscaling strategies towards improving the coarser resolution satellite soil moisture.</p>

scholarly journals 31 years of hourly spatially distributed air temperature, humidity, and precipitation amount and phase from Reynolds Critical Zone Observatory

2017 ◽  
pp. 1-11 ◽  
Author(s):  
Patrick R. Kormos ◽  
Danny G. Marks ◽  
Mark S. Seyfried ◽  
Scott C. Havens ◽  
Andrew Hedrick ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Air Temperature ◽  
Precipitation Amount ◽  
Critical Zone ◽  
Dew Point ◽  
Elevation Gradients ◽  
Wide Range ◽  
Critical Zone Observatory ◽  
Reynolds Creek ◽  
Spatially Distributed

Thirty one years of spatially distributed air temperature, relative humidity, dew point temperature, precipitation amount, and precipitation phase data are presented for the Reynolds Creek Experimental Watershed, which is part of the Critical Zone Observatory network. The air temperature, relative humidity, and precipitation amount data are spatially distributed over a 10&amp;thinsp;m Lidar-derived digital elevation model at an hourly time step using a detrended kriging algorithm. This dataset covers a wide range of weather extremes in a mesoscale basin (237&amp;thinsp;km<sup>2</sup>) that encompasses the rain-snow transition zone and should find widespread application in earth science modeling communities. Spatial data allows for a more holistic analysis of basin means and elevation gradients, compared to weather station data measured at specific locations. Files are stored in the NetCDF file format, which allows for easy spatiotemporal averaging and/or subsetting. Data are made publicly available through an OPeNDAP-enabled THREDDS server hosted by Boise State University Libraries in support of the Reynolds Creek Critical Zone Observatory (<a href="https://doi.org/doi:10.18122/B2B59V" target ="_blank">https://doi.org/10.18122/B2B59V</a>).

scholarly journals Comparing a Smartphone Irrigation Scheduling Application with Water Balance and Soil Moisture-based Irrigation Methods: Part I—Plasticulture-grown Tomato

2018 ◽  
Vol 28 (3) ◽  
pp. 354-361 ◽  
Author(s):  
Luke Miller ◽  
George Vellidis ◽  
Osama Mohawesh ◽  
Timothy Coolong
Keyword(s):  
Soil Moisture ◽  
Water Balance ◽  
Water Use ◽  
Meteorological Data ◽  
Irrigation Treatment ◽  
Irrigation Scheduling ◽  
Soluble Solids ◽  
Irrigation Regime ◽  
Plastic Mulch ◽  
Irrigation Water Use

A new smartphone vegetable irrigation scheduling application (VegApp) was compared with current irrigation scheduling recommendations and soil moisture sensor (SMS)–based irrigation for growing tomato (Solanum lycopersicum) in southern Georgia during Spring 2016 and 2017. Plants were grown using plastic mulch and drip irrigation following standard production. The VegApp-scheduled irrigation based on crop evapotranspiration (ETc) values calculated daily from meteorological data retrieved from nearby weather stations, whereas ETc rates for current water balance (WB)–based recommendations were calculated from historic averages for the region. Water usage, soil moisture tension, fruit yield and quality, and foliar macronutrient content were measured. In 2016, plants grown using SMS-based irrigation applied the least water followed by the VegApp- and WB-grown plants. In 2017, WB-treated plants received the least water, followed by VegApp- and SMS-grown plants. Total marketable yields were similar among treatments and years. Irrigation water use efficiency (IWUE) varied between year and irrigation regime, with SMS-grown plants having a significantly greater IWUE than the other treatments in 2016. Plants irrigated using the VegApp had a greater IWUE than SMS-irrigated plants in 2017. Differences in IWUE were largely the result of variable irrigation volumes and not changes in yield. Fruit total soluble solids (TSS) were unaffected by treatment in either study year. Fruit pH was affected by irrigation treatment in 2017. Foliar nitrogen concentrations were affected by irrigation regime in 2017, with VegApp-grown plants having significantly greater concentrations of foliar N than other irrigation treatments. The results of this study suggest that the VegApp could be a reliable tool that can be used by growers to produce yields comparable to currently accepted irrigation scheduling practices and reduce water use in some seasons.

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