scholarly journals The value of ASCAT soil moisture and MODIS snow cover data for calibrating a conceptual hydrologic model

2021 ◽  
Vol 25 (3) ◽  
pp. 1389-1410
Author(s):  
Rui Tong ◽  
Juraj Parajka ◽  
Andreas Salentinig ◽  
Isabella Pfeil ◽  
Jürgen Komma ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Snow Cover ◽  
Hydrological Modeling ◽  
Hydrologic Model ◽  
Catchment Scale ◽  
Data Set ◽  
Water Index ◽  
Soil Moisture Data ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. Recent advances in soil moisture remote sensing have produced satellite data sets with improved soil moisture mapping under vegetation and with higher spatial and temporal resolutions. In this study, we evaluate the potential of a new, experimental version of the Advanced Scatterometer (ASCAT) soil water index data set for multiple objective calibrations of a conceptual hydrologic model. The analysis is performed in 213 catchments in Austria for the period 2000–2014. An HBV (Hydrologiska Byråns Vattenbalansavdelning)-type hydrologic model is calibrated based on runoff data, ASCAT soil moisture data, and Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover data for various calibration variants. Results show that the inclusion of soil moisture data in the calibration mainly improves the soil moisture simulations, the inclusion of snow data mainly improves the snow simulations, and the inclusion of both of them improves both soil moisture and snow simulations to almost the same extent. The snow data are more efficient at improving snow simulations than the soil moisture data are at improving soil moisture simulations. The improvements of both runoff and soil moisture model efficiencies are larger in low elevation and agricultural catchments than in others. The calibrated snow-related parameters are strongly affected by including snow data and, to a lesser extent, by soil moisture data. In contrast, the soil-related parameters are only affected by the inclusion of soil moisture data. The results indicate that the use of multiple remote sensing products in hydrological modeling can improve the representation of hydrological fluxes and prediction of runoff hydrographs at the catchment scale.

scholarly journals The value of ASCAT soil moisture and MODIS snow cover data for calibrating a conceptual hydrologic model

2020 ◽  
Author(s):  
Rui Tong ◽  
Juraj Parajka ◽  
Andreas Salentinig ◽  
Isabella Pfeil ◽  
Jürgen Komma ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Snow Cover ◽  
Soil Water ◽  
Hydrologic Model ◽  
Multiple Objective ◽  
Water Index ◽  
Agricultural Catchments ◽  
Soil Moisture Data ◽  
Soil Moisture Model

Abstract. Recent advances in soil moisture remote sensing have produced satellite datasets with improved soil moisture mapping under vegetation and with higher spatial and temporal resolutions. In this study, we evaluate the potential of a new, experimental version of the ASCAT Soil Water Index dataset for multiple objective calibration of a conceptual hydrologic model. The analysis is performed in 213 catchments in Austria for the period 2000–2014. An HBV type hydrologic model is calibrated to runoff data, ASCAT soil moisture data, and MODIS snow cover data for various calibration variants. Results show that the inclusion of soil moisture data in the calibration mainly improves the soil moisture simulations; the inclusion of snow data mainly improves the snow simulations; and including both of them improves both soil moisture and snow simulations to almost the same extent. The snow data are more efficient in improving snow simulations than the soil moisture data are in improving soil moisture simulations. The improvements of both runoff and soil moisture model efficiencies are larger in low elevation and agricultural catchments than in others. The calibrated snow-related parameters are strongly affected by including snow data, and to a lesser extent by soil moisture data, while the soil-related parameters are only affected by the inclusion of soil moisture data.

scholarly journals From near-surface to root-zone soil moisture using an exponential filter: an assessment of the method based on in-situ observations and model simulations

2008 ◽  
Vol 12 (6) ◽  
pp. 1323-1337 ◽  
Author(s):  
C. Albergel ◽  
C. Rüdiger ◽  
T. Pellarin ◽  
J.-C. Calvet ◽  
N. Fritz ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Surface Soil ◽  
Root Zone ◽  
Synthetic Data ◽  
Surface Soil Moisture ◽  
Data Set ◽  
Water Index ◽  
In Situ Observations

Abstract. A long term data acquisition effort of profile soil moisture is under way in southwestern France at 13 automated weather stations. This ground network was developed in order to validate remote sensing and model soil moisture estimates. In this paper, both those in situ observations and a synthetic data set covering continental France are used to test a simple method to retrieve root zone soil moisture from a time series of surface soil moisture information. A recursive exponential filter equation using a time constant, T, is used to compute a soil water index. The Nash and Sutcliff coefficient is used as a criterion to optimise the T parameter for each ground station and for each model pixel of the synthetic data set. In general, the soil water indices derived from the surface soil moisture observations and simulations agree well with the reference root-zone soil moisture. Overall, the results show the potential of the exponential filter equation and of its recursive formulation to derive a soil water index from surface soil moisture estimates. This paper further investigates the correlation of the time scale parameter T with soil properties and climate conditions. While no significant relationship could be determined between T and the main soil properties (clay and sand fractions, bulk density and organic matter content), the modelled spatial variability and the observed inter-annual variability of T suggest that a weak climate effect may exist.

A regionally explicit, global SWI calibration based on ISMN observations

2021 ◽  
Author(s):  
Manolis G. Grillakis
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Large Scale ◽  
Root Zone ◽  
Soil Depth ◽  
European Space Agency ◽  
Soil Layer ◽  
Added Value ◽  
Water Index ◽  
Soil Moisture Data

<p>Remote sensing has proven to be an irreplaceable tool for monitoring soil moisture. The European Space Agency (ESA), through the Climate Change Initiative (CCI), has provided one of the most substantial contributions in the soil water monitoring, with almost 4 decades of global satellite derived and homogenized soil moisture data for the uppermost soil layer. Yet, due to the inherent limitations of many of the remote sensors, only a limited soil depth can be monitored. To enable the assessment of the deeper soil layer moisture from surface remotely sensed products, the Soil Water Index (SWI) has been established as a convolutive transformation of the surface soil moisture estimation, under the assumption of uniform hydraulic conductivity and the absence of transpiration. The SWI uses a single calibration parameter, the T-value, to modify its response over time.</p><p>Here the Soil Water Index (SWI) is calibrated using ESA CCI soil moisture against in situ observations from the International Soil Moisture Network and then use Artificial Neural Networks (ANNs) to find the best physical soil, climate, and vegetation descriptors at a global scale to regionalize the calibration of the T-value. The calibration is then used to assess a root zone related soil moisture for the period 2001 – 2018.</p><p>The results are compared against the European Centre for Medium-Range Weather Forecasts, ERA5 Land reanalysis soil moisture dataset, showing a good agreement, mainly over mid-latitudes. The results indicate that there is added value to the results of the machine learning calibration, comparing to the uniform T-value. This work contributes to the exploitation of ESA CCI soil moisture data, while the produced data can support large scale soil moisture related studies.</p>

Meteorological and hydrological conditions triggering rockfall events in Germany

2021 ◽  
Author(s):  
Katrin Nissen ◽  
Stefan Rupp ◽  
Björn Guse ◽  
Uwe Ulbrich ◽  
Sergiy Vorogushyn ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Soil Moisture ◽  
Statistical Model ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Daily Precipitation ◽  
Hydrologic Model ◽  
Data Set ◽  
Hydrological Conditions ◽  
Landslide Database

<p>In this study we present the results of a logistic regression model aimed at describing changes in probabilities for rockfall events in Germany in response to changes in meteorological and hydrological conditions.</p><p>The rockfall events for this study are taken from the landslide database for Germany (Damm and Klose, 2015). The meteorological variables we tested as predictors for the logistic regression model are daily precipitation from the REGNIE data set (Rauthe et al. 2013), hourly precipitation from the RADKLIM radar climatology (Winterrath et al., 2018) and temperature from the E-OBS data set (Cornes et al., 2018). As there is no observational soil moisture data set covering the entire country, we used soil moisture modelled with the state-of-the-art hydrological model mHM (Samaniego et al. 2010), which was calibrated using gauge measurements.</p><p>In order to select the best statistical model we tested a large number of physically plausible combinations of meteorological and hydrological predictors. Each model was checked using cross-validation. The decision on the final model was based on the value of the logarithmic skill score and on expert judgement.</p><p>The final statistical model includes the local percentile of daily precipitation, total relative soil moisture and freeze-thawing cycles in the previous weeks as predictors. It was found that daily precipitation is the most important parameter in the model. An increase of daily precipitation from its median to its 80th percentile approximately doubles the probability for a rockfall event. Higher soil moisture and the occurrence of freeze-thaw cycles also increase the probability for rockfall events. </p><p><br>Cornes, R. C. et al., 2018: An ensemble version of the E‐OBS temperature and precipitation data sets. Journal of Geophysical Research: Atmospheres, 123, 9391– 9409.</p><p>Damm, B., Klose, M., 2015. The landslide database for Germany: Closing the gap at national level. Geomorphology 249, 82–93</p><p>Rauthe, M. et al., 2013: A Central European precipitation climatology – Part I: Generation and validation of a high-reso-lution gridded daily data set (HYRAS), Vol. 22(3), p 235–256.</p><p>Samaniego, L. et al., 2010: Multiscale parameter regionalization of a grid-based hydrologic model at the mesoscale. Water Resour. Res., 46,W05523</p><p>Winterrath, T. et al., 2018: RADKLIM Version 2017.002: Reprocessed gauge-adjusted radar data, one-hour precipitation sums (RW), DOI: 10.5676/DWD/RADKLIM_RW_V2017.002.</p>

scholarly journals Effects of Winter Snow Cover on Spring Soil Moisture Based on Remote Sensing Data Product over Farmland in Northeast China

2020 ◽  
Vol 12 (17) ◽  
pp. 2716
Author(s):  
Shuang Liang ◽  
Xiaofeng Li ◽  
Xingming Zheng ◽  
Tao Jiang ◽  
Xiaojie Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Linear Regression ◽  
Snow Cover ◽  
Snow Depth ◽  
Northeast China ◽  
Regression Models ◽  
Linear Regression Models ◽  
Winter Snow ◽  
The Relationship

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.

The Assimilation of Remote Sensing-Derived Soil Moisture Data into a Hydrological Model for the Mahanadi Basin, India

2019 ◽  
Vol 47 (8) ◽  
pp. 1357-1374 ◽  
Author(s):  
Soumya S. Behera ◽  
Bhaskar Ramchandra Nikam ◽  
Mukund S. Babel ◽  
Vaibhav Garg ◽  
Shiv Prasad Aggarwal
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Hydrological Model ◽  
Soil Moisture Data ◽  
Mahanadi Basin

scholarly journals Potential and limitations of multidecadal satellite soil moisture observations for climate model evaluation studies

2013 ◽  
Vol 10 (3) ◽  
pp. 3541-3594 ◽  
Author(s):  
A. Loew ◽  
T. Stacke ◽  
W. Dorigo ◽  
R. de Jeu ◽  
S. Hagemann
Keyword(s):  
Soil Moisture ◽  
Model Evaluation ◽  
Climate Model ◽  
Global Climate Models ◽  
Satellite Observations ◽  
Data Set ◽  
Soil Moisture Dynamics ◽  
Soil Moisture Data ◽  
Climate Model Evaluation ◽  
Moisture Dynamics

Abstract. Soil moisture is an essential climate variable of major importance for land-atmosphere interactions and global hydrology. An appropriate representation of soil moisture dynamics in global climate models is therefore important. Recently, a first multidecadal, observational based soil moisture data set has become available that provides information on soil moisture dynamics from satellite observations (ECVSM). The present study investigates the potential and limitations of this new dataset for several applications for climate model evaluation. We compare soil moisture data from satellite observations, reanalysis data and simulation results from a state-of-the-art climate model and analyze relationships between soil moisture and precipitation anomalies in the different datasets. In a detailed regional study, we show that ECVSM is capable to capture well interannual and intraannual soil moisture and precipitation dynamics in the Sahelian region. Current deficits of the new dataset are critically discussed and summarized at the end of the paper to provide guidance for an appropriate usage of the ECVSM dataset for climate studies.

Sign in / Sign up

Export Citation Format

Share Document