Combining static and portable Cosmic Ray Neutron Sensor data to assess catchment scale heterogeneity in soil water storage and their integrated role in catchment runoff response

2021 ◽  
pp. 126659
Author(s):  
Katya Dimitrova-Petrova ◽  
Rafael Rosolem ◽  
Chris Soulsby ◽  
Mark E. Wilkinson ◽  
Allan Lilly ◽  
...  
Keyword(s):  
Soil Water ◽  
Water Storage ◽  
Cosmic Ray ◽  
Sensor Data ◽  
Soil Water Storage ◽  
Catchment Scale ◽  
Scale Heterogeneity ◽  
Catchment Runoff

scholarly journals Calibration of a non-invasive cosmic-ray probe for wide area snow water equivalent measurement

2016 ◽  
Vol 10 (3) ◽  
pp. 1181-1190 ◽  
Author(s):  
Mark J. P. Sigouin ◽  
Bing C. Si
Keyword(s):  
Remote Sensing ◽  
Linear Regression ◽  
Soil Water ◽  
Snow Water Equivalent ◽  
Pearson Correlation ◽  
Water Storage ◽  
Cosmic Ray ◽  
Soil Water Storage ◽  
Neutron Intensity ◽  
Snow Water

Abstract. Measuring snow water equivalent (SWE) is important for many hydrological purposes such as modelling and flood forecasting. Measurements of SWE are also crucial for agricultural production in areas where snowmelt runoff dominates spring soil water recharge. Typical methods for measuring SWE include point measurements (snow tubes) and large-scale measurements (remote sensing). We explored the potential of using the cosmic-ray soil moisture probe (CRP) to measure average SWE at a spatial scale between those provided by snow tubes and remote sensing. The CRP measures above-ground moderated neutron intensity within a radius of approximately 300 m. Using snow tubes, surveys were performed over two winters (2013/2014 and 2014/2015) in an area surrounding a CRP in an agricultural field in Saskatoon, Saskatchewan, Canada. The raw moderated neutron intensity counts were corrected for atmospheric pressure, water vapour, and temporal variability of incoming cosmic-ray flux. The mean SWE from manually measured snow surveys was adjusted for differences in soil water storage before snowfall between both winters because the CRP reading appeared to be affected by soil water below the snowpack. The SWE from the snow surveys was negatively correlated with the CRP-measured moderated neutron intensity, giving Pearson correlation coefficients of −0.90 (2013/2014) and −0.87 (2014/2015). A linear regression performed on the manually measured SWE and moderated neutron intensity counts for 2013/2014 yielded an r2 of 0.81. Linear regression lines from the 2013/2014 and 2014/2015 manually measured SWE and moderated neutron counts were similar; thus differences in antecedent soil water storage did not appear to affect the slope of the SWE vs. neutron relationship. The regression equation obtained from 2013/2014 was used to model SWE using the moderated neutron intensity data for 2014/2015. The CRP-estimated SWE for 2014/2015 was similar to that of the snow survey, with an root-mean-square error of 8.8 mm. The CRP-estimated SWE also compared well to estimates made using snow depths at meteorological sites near (< 10 km) the CRP. Overall, the empirical equation presented provides acceptable estimates of average SWE using moderated neutron intensity measurements. Using a CRP to monitor SWE is attractive because it delivers a continuous reading, can be installed in remote locations, requires minimal labour, and provides a landscape-scale measurement footprint.

scholarly journals Calibration of a non-invasive cosmic-ray probe for wide area snow water equivalent measurement

2016 ◽  
Author(s):  
M. J. P. Sigouin ◽  
B. C. Si
Keyword(s):  
Remote Sensing ◽  
Linear Regression ◽  
Soil Water ◽  
Snow Water Equivalent ◽  
Water Storage ◽  
Cosmic Ray ◽  
Measurement Scale ◽  
Soil Water Storage ◽  
Neutron Intensity ◽  
Snow Water

Abstract. Measuring snow water equivalent (SWE) is important for many hydrological purposes such as modeling and flood forecasting. Measurements of SWE are also crucial for agricultural production in areas where snowmelt runoff dominates spring soil water recharge. Typical methods for measuring SWE include point measurements (snow tubes) and large-scale measurements (remote sensing). We explored the potential of using the cosmic-ray soil moisture probe (CRP) to measure average SWE at a measurement scale between those provided by snow tubes and remote sensing. The CRP measures above ground moderated neutron intensity within a radius of approximately 300 m. Using snow tubes, surveys were performed over two winters (2013/2014 and 2014/2015) in an area surrounding a CRP in an agricultural field in Saskatoon, Saskatchewan, CAN. The raw moderated neutron intensity counts were corrected for atmospheric pressure, water vapor, and temporal variability of incoming cosmic ray flux. The mean SWE from manually measured snow surveys was adjusted for differences in soil water storage before snowfall between both winters because the CRP reading appeared to be affected by soil water below the snowpack. The SWE from the snow surveys was negatively correlated with the CRP-measured moderated neutron intensity, giving Pearson correlation coefficients of −0.92 (2013/2014) and −0.94 (2014/2015). A linear regression performed on the manually measured SWE and moderated neutron intensity counts for 2013/2014 yielded an r2 of 0.84. Linear regression lines from the 2013/2014 and 2014/2015 manually measured SWE and moderated neutron counts were very similar, thus differences in antecedent soil water storage did not appear to affect the slope of the SWE vs. neutron relationship. The regression equation obtained from 2013/2014 was used to model SWE using the moderated neutron intensity data for 2014/2015. The CRP-estimated SWE for 2014/2015 was similar to that of the snow survey, with a RMSE of 7.7 mm. The CRP-estimated SWE also compared well to estimates made using snow depths at meteorological sites near (< 10 km) the CRP. Overall, the empirical equation presented provides acceptable estimates of average SWE using moderated neutron intensity measurements. Using a CRP to monitor SWE is attractive because it delivers a continuous reading, can be installed in remote locations, requires minimal labour, and provides a landscape-scale measurement footprint.

scholarly journals Analysis of the drought recovery of Andosols on southern Ecuadorian Andean páramos

2016 ◽  
Vol 20 (6) ◽  
pp. 2421-2435 ◽  
Author(s):  
Vicente Iñiguez ◽  
Oscar Morales ◽  
Felipe Cisneros ◽  
Willy Bauwens ◽  
Guido Wyseure
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Potential Evapotranspiration ◽  
Water Storage ◽  
Dry Season ◽  
Time Domain Reflectometry ◽  
Soil Water Storage ◽  
Wet Season ◽  
Catchment Scale ◽  
Normal Value

Abstract. The Neotropical Andean grasslands above 3500 m a.s.l., known as páramo, offer remarkable ecological services for the Andean region. The most important of these is the water supply of excellent quality to many cities and villages in the inter-Andean valleys and along the coast. The páramo ecosystem and especially its soils are under constant and increased threat by human activities and climate change. In this study, the recovery speed of the páramo soils after drought periods are analysed. The observation period includes the droughts of 2009, 2010, 2011, and 2012 together with intermediate wet periods. Two experimental catchments – one with and one without páramo – were investigated. The Probability Distributed Moisture (PDM) model was calibrated and validated in both catchments. Drought periods and its characteristics were identified and quantified by a threshold level approach and complemented by means of a drought propagation analysis. At the plot scale in the páramo region, the soil water content measured by time domain reflectometry (TDR) probes dropped from a normal value of about 0.84 to  ∼ 0.60 cm3 cm−3, while the recovery time was 2–3 months. This did not occur at lower altitudes (Cumbe) where the soils are mineral. Although the soil moisture depletion observed in these soils was similar to that of the Andosols (27 %), decreasing from a normal value of about 0.54 to  ∼ 0.39 cm3 cm−3, the recovery was much slower and took about 8 months for the drought in 2010. At the catchment scale, however, the soil water storage simulated by the PDM model and the drought analysis was not as pronounced. Soil moisture droughts occurred mainly in the dry season in both catchments. The deficit for all cases is small and progressively reduced during the wet season. Vegetation stress periods correspond mainly to the months of September, October and November, which coincides with the dry season. The maximum number of consecutive dry days were reached during the drought of 2009 and 2010 (19 and 22 days), which can be considered to be a long period in the páramo. The main factor in the hydrological response of these experimental catchments is the precipitation relative to the potential evapotranspiration. As the soils never became extremely dry nor close to the wilting point, the soil water storage capacity had a secondary influence.

scholarly journals From Patch to Catchment: A Statistical Framework to Identify and Map Soil Moisture Patterns Across Complex Alpine Terrain

2020 ◽  
Vol 2 ◽  
Author(s):  
Anna L. Hermes ◽  
Haruko M. Wainwright ◽  
Oliver Wigmore ◽  
Nicola Falco ◽  
Noah P. Molotch ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Clustering Algorithm ◽  
Stream Water ◽  
Water Storage ◽  
Plant Ecology ◽  
Supervised Machine Learning ◽  
Soil Water Storage ◽  
Catchment Scale ◽  
Solute Fluxes

Climate warming in alpine regions is changing patterns of water storage, a primary control on alpine plant ecology, biogeochemistry, and water supplies to lower elevations. There is an outstanding need to determine how the interacting drivers of precipitation and the critical zone (CZ) dictate the spatial pattern and time evolution of soil water storage. In this study, we developed an analytical framework that combines intensive hydrologic measurements and extensive remotely-sensed observations with statistical modeling to identify areas with similar temporal trends in soil water storage within, and predict their relationships across, a 0.26 km2 alpine catchment in the Colorado Rocky Mountains, U.S.A. Repeat measurements of soil moisture were used to drive an unsupervised clustering algorithm, which identified six unique groups of locations ranging from predominantly dry to persistently very wet within the catchment. We then explored relationships between these hydrologic groups and multiple CZ-related indices, including snow depth, plant productivity, macro- (102-&gt;103 m) and microtopography (&lt;100-102 m), and hydrological flow paths. Finally, we used a supervised machine learning random forest algorithm to map each of the six hydrologic groups across the catchment based on distributed CZ properties and evaluated their aggregate relationships at the catchment scale. Our analysis indicated that ~40–50% of the catchment is hydrologically connected to the stream channel, lending insight into the portions of the catchment that likely dominate stream water and solute fluxes. This research expands our understanding of patch-to-catchment-scale physical controls on hydrologic and biogeochemical processes, as well as their relationships across space and time, which will inform predictive models aimed at determining future changes to alpine ecosystems.

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.

scholarly journals Energy balance closure on a winter wheat stand: comparing the eddy covariance technique with the soil water balance method

2016 ◽  
Vol 13 (1) ◽  
pp. 63-75 ◽  
Author(s):  
K. Imukova ◽  
J. Ingwersen ◽  
M. Hevart ◽  
T. Streck
Keyword(s):  
Energy Balance ◽  
Water Balance ◽  
Winter Wheat ◽  
Water Content ◽  
Soil Water ◽  
Water Storage ◽  
Soil Water Balance ◽  
Soil Water Storage ◽  
Water Balance Method ◽  
Closure Method

Abstract. The energy balance of eddy covariance (EC) flux data is typically not closed. The nature of the gap is usually not known, which hampers using EC data to parameterize and test models. In the present study we cross-checked the evapotranspiration data obtained with the EC method (ETEC) against ET rates measured with the soil water balance method (ETWB) at winter wheat stands in southwest Germany. During the growing seasons 2012 and 2013, we continuously measured, in a half-hourly resolution, latent heat (LE) and sensible (H) heat fluxes using the EC technique. Measured fluxes were adjusted with either the Bowen-ratio (BR), H or LE post-closure method. ETWB was estimated based on rainfall, seepage and soil water storage measurements. The soil water storage term was determined at sixteen locations within the footprint of an EC station, by measuring the soil water content down to a soil depth of 1.5 m. In the second year, the volumetric soil water content was additionally continuously measured in 15 min resolution in 10 cm intervals down to 90 cm depth with sixteen capacitance soil moisture sensors. During the 2012 growing season, the H post-closed LE flux data (ETEC =  3.4 ± 0.6 mm day−1) corresponded closest with the result of the WB method (3.3 ± 0.3 mm day−1). ETEC adjusted by the BR (4.1 ± 0.6 mm day−1) or LE (4.9 ± 0.9 mm day−1) post-closure method were higher than the ETWB by 24 and 48 %, respectively. In 2013, ETWB was in best agreement with ETEC adjusted with the H post-closure method during the periods with low amount of rain and seepage. During these periods the BR and LE post-closure methods overestimated ET by about 46 and 70 %, respectively. During a period with high and frequent rainfalls, ETWB was in-between ETEC adjusted by H and BR post-closure methods. We conclude that, at most observation periods on our site, LE is not a major component of the energy balance gap. Our results indicate that the energy balance gap is made up by other energy fluxes and unconsidered or biased energy storage terms.

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