Scholarly record
EVALUATION OF HYDROLOGICAL BALANCE USING SOIL MOISTURE
Abstract
Several spatially distributed soil moisture estimates of different origin, spatial and temporal resolution are currently available for Europe or individual countries. These estimates range from gridded observations and satellite products to the estimates of large scale and/or spatially distributed water balance models. The estimated soil moisture is potentially useful for improving the water balance assessment with hydrological models in gauged and ungauged basins and reduction of uncertainty in the estimated parameters of the hydrological model. The aim of this study is to evaluate this potential. This is done by (1) direct incorporation of soil moisture estimates as input to the hydrological model, (2) calibrating the hydrological model with respect to soil moisture estimates and (3) constraining relevant parameter of hydrological model based on soil moisture estimates. First two strategies are tested in situations when observed runoff is available and when only basic characteristics of runoff (mean, runoff ratio) are available. The added value of soil moisture estimates is evaluated with respect to prediction at ungauged basins and reduction of uncertainty in estimated parameters.
Publication Impact Profile
Publication details
References10
Hlavinka P., Trnka M., Balek J., Semerádová D., Hayes M., Svoboda M., Eitzinger J., Možný M., Fischer M., Hunt E., Žalud Z., Development and evaluation of the SoilClim model for water balance and soil climate estimates, Agriculture and Water Management, 98(8), 1249–1261, 2011.
Vizina A., Hanel M., Novický O., Treml, P., Experience from Simulation of Climate Impacts on Water Regime in Monthly and Daily Time Step, T. G. Masaryk Water Research Institute, p.r.i, Prague, 2010.
Kašpárek L., Hanel M., Horáček S., Máca, Vizina A., BILAN water balance model. R package version 2016-10-20, T. G. Masaryk WRI, p.r.i, Prague, 2016.
T.G.Masaryk Water Research Institute in Prague, p.r.i., BILAN water balance model – Manual, https://bilan.vuv.cz/manual, 2016.
R Core Team R, A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/, 2016.
Mauricio Zambrano-Bigiarini, hydroGOF: Goodness-of-fit functions for comparison of simulated and observed hydrological time series. R package version 0.3-8, https://CRAN.R-project.org/package=hydroGOF, 2014.
Matt Dowle and Arun Srinivasan, data.table: Extension of `data.frame`. R package version 1.10.4. https://CRAN.R-project.org/package=data.table, 2017.
Wickam H., ggplot2: Elegant Graphics for Data Analysis, Springer-Verlag New York, 78-0-387-98140-6, https://ggplot2.org, 2009.
Gupta, Hoshin V., Harald Kling, Koray K. Yilmaz, Guillermo F. Martinez. Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling. Journal of Hydrology, Volume 377, Issues 1-2, Pages 80-91. DOI: 10.1016/j.jhydrol.2009.08.003. ISSN 0022-1694, 2009.
Klingw, H., M. Fuchs, and M. Paulin, Runoff conditions in the upper Danube basin under an ensemble of climate change scenarios. Journal of Hydrology, Volumes 424 425, 6 March 2012, Pages 264-277, DOI: 10.1016/j.jhydrol.2012.01.011, 2012.
View or Download full articleAccess options
SWS access login
Login as SWS Scientific CommitteeLogin as SWS Scientific PartnerLogin as SWS AuthorAuthors and approved SWS contributors will read and export their own linked papers after identity matching by SWS profile, email and SGEM GlobalID.
For librarian assistance: [email protected]
Purchase Instant Access
- Article can be downloaded after successful payment.
- Article may be used according to SWS library access terms.
- Article cannot be redistributed.


