32 citations as recorded by crossref.

1. A phosphorus index that combines critical source areas and transport pathways using a travel time approach B. Buchanan et al. https://doi.org/10.1016/j.jhydrol.2013.01.018
2. On the Disaggregation of Passive Microwave Soil Moisture Data UsingA PrioriKnowledge of Temporally Persistent Soil Moisture Fields A. Loew & W. Mauser https://doi.org/10.1109/TGRS.2007.914800
3. 2-way coupling the hydrological land surface model PROMET with the regional climate model MM5 F. Zabel & W. Mauser https://doi.org/10.5194/hess-17-1705-2013
4. A Meteorological Distribution System for High-Resolution Terrestrial Modeling (MicroMet) G. Liston & K. Elder https://doi.org/10.1175/JHM486.1
5. Comparison of the estimated water and energy budgets of a large winter wheat field during AgriSAR 2006 by multiple sensors and models V. Pauwels et al. https://doi.org/10.1016/j.jhydrol.2007.11.016
6. ArcE: A GIS tool for modelling actual evapotranspiration S. España et al. https://doi.org/10.1016/j.cageo.2011.03.008
7. The SMOS Validation Campaign 2010 in the Upper Danube Catchment: A Data Set for Studies of Soil Moisture, Brightness Temperature, and Their Spatial Variability Over a Heterogeneous Land Surface J. dall'Amico et al. https://doi.org/10.1109/TGRS.2012.2196523
8. Remote sensing and GIS for estimation of irrigation crop water demand I. El‐Magd & T. Tanton https://doi.org/10.1080/0143116042000298261
9. High-resolution fully coupled atmospheric–hydrological modeling: a cross-compartment regional water and energy cycle evaluation B. Fersch et al. https://doi.org/10.5194/hess-24-2457-2020
10. PROMET – Large scale distributed hydrological modelling to study the impact of climate change on the water flows of mountain watersheds W. Mauser & H. Bach https://doi.org/10.1016/j.jhydrol.2009.07.046
11. Advances in Snow Hydrology Using a Combined Approach of GNSS In Situ Stations, Hydrological Modelling and Earth Observation—A Case Study in Canada F. Appel et al. https://doi.org/10.3390/geosciences9010044
12. Mesoscale water balance modelling in the Upper Danube watershed using sub-scale land cover information derived from NOAA-AVHRR imagery and GIS-techniques R. Ludwig et al. https://doi.org/10.1016/j.pce.2003.09.011
13. A pragmatic approach for the downscaling and bias correction of regional climate simulations: evaluation in hydrological modeling T. Marke et al. https://doi.org/10.5194/gmd-4-759-2011
14. First Results of SMOS Soil Moisture Validation in the Upper Danube Catchment J. dall'Amico et al. https://doi.org/10.1109/TGRS.2011.2171496
15. Using a Remote Sensing-Supported Hydro-Agroecological Model for Field-Scale Simulation of Heterogeneous Crop Growth and Yield: Application for Wheat in Central Europe T. Hank et al. https://doi.org/10.3390/rs70403934
16. Application of a hydrometeorological model chain to investigate the effect of global boundaries and downscaling on simulated river discharge T. Marke et al. https://doi.org/10.1007/s12665-013-2876-z
17. A framework to improve hyper-resolution hydrological simulation in snow-affected regions X. Shen & E. Anagnostou https://doi.org/10.1016/j.jhydrol.2017.05.048
18. Analysis of SMOS brightness temperature and vegetation optical depth data with coupled land surface and radiative transfer models in Southern Germany F. Schlenz et al. https://doi.org/10.5194/hess-16-3517-2012
19. Validation of digital elevation models from SRTM X-SAR for applications in hydrologic modeling R. Ludwig & P. Schneider https://doi.org/10.1016/j.isprsjprs.2006.05.003
20. Semi‐distributed modelling of the monthly water balance in an alpine catchment G. Eder et al. https://doi.org/10.1002/hyp.5888
21. Uncertainty Assessment of the SMOS Validation in the Upper Danube Catchment F. Schlenz et al. https://doi.org/10.1109/TGRS.2011.2171694
22. Comparing Local Analysis and Prediction System (LAPS) Assimilations with Independent Observations C. Hiemstra et al. https://doi.org/10.1175/WAF961.1
23. A Geomorphic Approach for Identifying Flash Flood Potential Areas in the East Rapti River Basin of Nepal T. Pangali Sharma et al. https://doi.org/10.3390/ijgi10040247
24. Current Awareness https://doi.org/10.1002/hyp.469
25. SRTM DEM and its application advances L. Yang et al. https://doi.org/10.1080/01431161003786016
26. PredictingEllenberg's soil moisture indicator value in theBavarianAlps using additive georegression T. Häring et al. https://doi.org/10.1111/j.1654-109X.2012.01210.x
27. Rigorous evaluation of a soil heat transfer model for mesoscale climate change impact studies M. Muerth & W. Mauser https://doi.org/10.1016/j.envsoft.2012.02.017
28. Climate change impact assessment under data scarcity by hydrological and hydrodynamic modeling in Izmit Bay/Turkey G. Engin et al. https://doi.org/10.35208/ert.777323
29. The role of hydrological model complexity and uncertainty in climate change impact assessment R. Ludwig et al. https://doi.org/10.5194/adgeo-21-63-2009
30. The European heat wave 2003: Early indicators from multisensoral microwave remote sensing? A. Loew et al. https://doi.org/10.1029/2008JD010533
31. Soil moisture retrieval through a merging of multi-temporal L-band SAR data and hydrologic modelling F. Mattia et al. https://doi.org/10.5194/hess-13-343-2009
32. Evaluating topographic wetness indices across central New York agricultural landscapes B. Buchanan et al. https://doi.org/10.5194/hess-18-3279-2014