GNSS reflectometry measurement of snow depth and soil moisture in the French Alps

Author(s):  
K. Boniface ◽  
A. Walpersdorf ◽  
G. Guyomarc'h ◽  
Y. Deliot ◽  
F. Karbou ◽  
...  
2021 ◽  
Author(s):  
Paulo de Tarso Setti Junior ◽  
Tonie van Dam

<p>Soil moisture is an essential climate variable, influencing geophysical and hydrological processes such as vegetation and agriculture, land-atmosphere circulation, and drought development. It is possible to remotely sense soil moisture based on the dielectric constant of soil at microwave frequencies. Low earth orbit (LEO) satellites are capable of receiving Global Navigation Satellite Systems (GNSS) signals reflected off the surface of the Earth to infer properties of the reflecting surface itself, in a technique known as GNSS-Reflectometry (GNSS-R). However, converting surface reflectivity derived from GNSS-R into soil moisture is not straightforward. Reflectivity is influenced by other factors such as the vegetation optical depth and the soil roughness around the specular reflection. The Cyclone Global Navigation Satellite System (CYGNSS) is a mission from the National Aeronautics and Space Administration (NASA) consisting of eight small GNSS-R satellites with the primary objective of measuring wind speed in hurricanes and tropical cyclones. The satellites were launched in December 2016 in a 35° inclination orbit, and the measurements are made of reflected Global Positioning System (GPS) L1 (1.575 GHz) navigation signals. Reflections over land can be used to estimate soil moisture in the upper 5 cm of soil surface if they are correctly treated and modelled. In this work, we use three years of observations from CYGNSS mission (March 2017 - March 2020) to compute surface reflectivity over land assuming coherent reflections. Using linear regression models and ancillary information from Soil Moisture Active Passive (SMAP) mission (soil moisture, vegetation optical depth, and roughness coefficient), these reflectivity observations are then used to estimate soil moisture. Retrievals are compared with observations from 44 in-situ soil moisture stations from the International Soil Moisture Network (ISMN) in the Contiguous United States (CONUS), presenting in most of the cases a good agreement. Results are also correlated with vegetation optical depth, surface roughness, and topographic relief around the in-situ stations. In addition, some challenges regarding soil moisture estimation using spaceborne GNSS-R data are presented and discussed.</p>


2020 ◽  
Vol 12 (17) ◽  
pp. 2716
Author(s):  
Shuang Liang ◽  
Xiaofeng Li ◽  
Xingming Zheng ◽  
Tao Jiang ◽  
Xiaojie Li ◽  
...  

Spring soil moisture (SM) is of great importance for monitoring agricultural drought and waterlogging in farmland areas. While winter snow cover has an important impact on spring SM, relatively little research has examined the correlation between winter snow cover and spring SM in great detail. To understand the effects of snow cover on SM over farmland, the relationship between winter snow cover parameters (maximum snow depth (MSD) and average snow depth (ASD)) and spring SM in Northeast China was examined based on 30 year passive microwave snow depth (SD) and SM remote-sensing products. Linear regression models based on winter snow cover were established to predict spring SM. Moreover, 4 year SD and SM data were applied to validate the performance of the linear regression models. Additionally, the effects of meteorological factors on spring SM also were analyzed using multiparameter linear regression models. Finally, as a specific application, the best-performing model was used to predict the probability of spring drought and waterlogging in farmland in Northeast China. Our results illustrated the positive effects of winter snow cover on spring SM. The average correlation coefficient (R) of winter snow cover and spring SM was above 0.5 (significant at a 95% confidence level) over farmland. The performance of the relationship between snow cover and SM in April was better than that in May. Compared to the multiparameter linear regression models in terms of fitting coefficient, MSD can be used as an important snow parameter to predict spring drought and waterlogging probability in April. Specifically, if the relative SM threshold is 50% when spring drought occurs in April, the prediction probability of the linear regression model concerning snow cover and spring SM can reach 74%. This study improved our understanding of the effects of winter snow cover on spring SM and will be beneficial for further studies on the prediction of spring drought.


Author(s):  
Sibylle Vey ◽  
Andreas Guntner ◽  
Jens Wickert ◽  
Theresa Blume ◽  
Heiko Thoss ◽  
...  

1993 ◽  
Vol 73 (4) ◽  
pp. 489-501 ◽  
Author(s):  
H. N. Hayhoe ◽  
R. G. Pelletier ◽  
L. J. P. van Vliet

Rainfall and snowmelt runoff on soil frozen below the surface are recognized as important factors contributing to soil loss in Canada. The risk of rain on frozen soil has been quantified, and the amount of snowmelt on frozen soil has been estimated. This study extends such research to derive a climate-based model to estimate winter and spring runoff. This could result in a more accurate erosion prediction for areas where snowmelt is a major source for runoff. Selected components of the Water Erosion Prediction Project (WEPP) model and the versatile soil moisture budget (VB) were tested on observed data for two study sites in the Peace River region. The version of the WEPP model available to us estimated snow depth, soil frost depth and frequency of freeze–thaw cycles. However, the results did not adequately match observed data. The VB was modified in this study to improve the estimate of potential winter and spring runoff, and it was shown that incorporating observations of snow depth improved the estimate of the time and amount of snowmelt runoff. The modified runoff model was validated with data collected in the Peace River area of northern Alberta and British Columbia and with published data from the Prairies. Key words: Snowmelt, runoff, soil moisture budget


2013 ◽  
Vol 14 (3) ◽  
pp. 787-807 ◽  
Author(s):  
Hua Su ◽  
Robert E. Dickinson ◽  
Kirsten L. Findell ◽  
Benjamin R. Lintner

Abstract The response of the warm-season atmosphere to antecedent snow anomalies has long been an area of study. This paper explores how the spring snow depth relates to subsequent precipitation in central Canada using ground observations, reanalysis datasets, and offline land surface model estimates. After removal of low-frequency ocean influences, April snow depth is found to correlate negatively with early warm-season (May–June) precipitation across a large portion of the study area. A chain of mechanisms is hypothesized to account for this observed negative relation: 1) a snow depth anomaly leads to a soil moisture anomaly, 2) the subsequent soil moisture anomaly affects ground turbulent fluxes, and 3) the atmospheric vertical structure allows dry soil to promote local convection. A detailed analysis supports this chain of mechanisms for those portions of the domain manifesting a statistically significant negative snow–precipitation correlation. For a portion of the study area, large-scale atmospheric circulation patterns also affect the early warm-season rainfall, indicating that the snow–precipitation feedback may depend on large-scale atmospheric dynamical features. This analysis suggests that spring snow conditions can contribute to warm-season precipitation predictability on a subseasonal to seasonal scale, but that the strength of such predictability varies geographically as it depends on the interplay of hydroclimatological conditions across multiple spatial scales.


Anales AFA ◽  
2020 ◽  
pp. 90-100
Author(s):  
E. N. Gomes ◽  
J. Arellana ◽  
M. Franco ◽  
F. Grings ◽  
E. More

In this work we present an innovative and low-cost way to estimate the dielectric constant of real soils -which is relatedto soil moisture- using GPS satellite signal. The trajectory of the satellites was followed with an automated device, andwith an antenna type patch was measured the interference pattern generated by the direct GPS signal and that reflectedby the ground. To analyze the acquired signal, an electromagnetic dispersion model based on the method of smallperturbation was used, which may include stratifications in the underground and from which the dielectric constant thatbest fits the measurements was obtained. These values were compared with the obteined using a sensor that measures thesoil dielectric constant textit in-situ. The comparisons shows that the method based on the interference pattern providessatisfactory results to estimate the dielectric constant of real bare soils. Finally, proposals are outlined to improve theresults obtained in this work.


2020 ◽  
Author(s):  
Erwan Le Roux ◽  
Guillaume Evin ◽  
Nicolas Eckert ◽  
Juliette Blanchet ◽  
Samuel Morin

Abstract. In a context of climate change, trends in extreme snow loads need to be determined to minimize the risk of structure collapse.We study trends in annual maxima of ground snow load (GSL) using non-stationary extreme value models. Trends in return levels of GSL are assessed at a mountain massif scale from GSL data, provided for the French Alps from 1959 to 2019 by a meteorological reanalysis and a snowpack model. Our results indicate a temporal decrease in 50-year return levels from 900 m to 4200 m, significant in the Northwest of the French Alps until 2100 m. Despite this decrease, in half of the massifs, the return level in 2019 at 1800 m exceeds the return level designed for French building standards under a stationary assumption. We believe that this high number of exceedances is due to questionable assumptions concerning the computation of current standards. For example, these were devised with GSL, estimated from snow depth and constant snow density set to 150 kg m−3, which underestimate typical GSL values for the full snowpack.


2009 ◽  
Vol 48 (12) ◽  
pp. 2487-2512 ◽  
Author(s):  
Yves Durand ◽  
Gérald Giraud ◽  
Martin Laternser ◽  
Pierre Etchevers ◽  
Laurent Mérindol ◽  
...  

Abstract Since the early 1990s, Météo-France has used an automatic system combining three numerical models to simulate meteorological parameters, snow cover stratigraphy, and avalanche risk at various altitudes, aspects, and slopes for a number of mountainous regions (massifs) in the French Alps and the Pyrenees. This Système d’Analyse Fournissant des Renseignements Atmosphériques à la Neige (SAFRAN)–Crocus–Modèle Expert de Prévision du Risque d’Avalanche (MEPRA) model chain (SCM), usually applied to operational daily avalanche forecasting, is here used for retrospective snow and climate analysis. For this study, the SCM chain used both meteorological observations and guess fields mainly issued from the newly reanalyzed atmospheric model 40-yr ECMWF Re-Analysis (ERA-40) data and ran on an hourly basis over a period starting in the winter of 1958/59 until recent past winters. Snow observations were finally used for validation, and the results presented here concern only the main climatic features of the alpine modeled snowfields at different spatial and temporal scales. The main results obtained confirm the very significant spatial and temporal variability of the modeled snowfields with regard to certain key parameters such as those describing ground coverage or snow depth. Snow patterns in the French Alps are characterized by a marked declining gradient from the northwestern foothills to the southeastern interior regions. This applies mainly to both depths and durations, which exhibit a maximal latitudinal variation at 1500 m of about 60 days, decreasing strongly with the altitude. Enhanced at low elevations, snow depth shows a mainly negative temporal variation over the study period, especially in the north and during late winters, while the south exhibits more smoothed features. The number of days with snow on the ground shows also a significant general signal of decrease at low and midelevation, but this signal is weaker in the south than in the north and less visible at high elevation. Even if a statistically significant test cannot be performed for all elevations and areas, the temporal decrease is present in all the studied quantities. Concerning snow duration, this general decrease can also be interpreted as a sharp variation of the mean values at the end of the 1980s, inducing a step effect in its time series rather than a constant negative temporal trend. The results have also been interpreted in terms of potential for a viable ski industry, especially in the southern areas, and for different changing climatic conditions. Presently, French downhill ski resorts are economically viable from a range of about 1200 m MSL in the northern foothills to 2000 m in the south, but future prospects are uncertain. In addition, no clear and direct relationship between the North Atlantic Oscillation (NAO) or the ENSO indexes and the studied snow parameters could be established in this study.


2021 ◽  
Author(s):  
Maxim Kharlamov ◽  
Maria Kireeva ◽  
Natalia Varentsova

<p>Over the past 20 years, the climate on the East European plain tends to be significantly warmer and drier. Winters became shorter and spring freshet’s conditions have been changed significantly. Maximum snow depth was the most important factor of spring freshet formation 30 years ago, but nowadays it has no significance at all and main factor today is melt water losses on infiltration and evaporation.</p><p>We registered a decrease in the period of stable snow accumulation (on average by 20% in the southern and southwestern parts of the East European Plain) because of the increase in winter temperatures. More often during first part of winter snow cover disappeared totally. The number of thaws and their duration at the end of the winter also increase and this leads to earlier and more prolonged melting of the snow pack. In these conditions, an extremely low spring freshet is formed. Our studies show that with the condition of an equal maximum snow depth the slow snowmelt forms the spring freshet up to 4 times less in volume than the fast melting.</p><p>Soil moisture also plays an important role in the melt water losses. The most part of the East European Plain is characterized by a decrease in soil moisture in late autumn, which indicates increased losses during snow melting period.</p><p>Still, the most significant changes in the structure of the factors of spring freshet formation are common to the southern and southwestern parts of the East European Plain. In the northern part, conservative factors still dominate, although this area is characterized by the significant increase in winter temperatures.</p><p>The study was supported by Russian Science Foundation Proj. №19-77-10032</p>


Sign in / Sign up

Export Citation Format

Share Document