Integrating hydrological modelling, data assimilation and cloud computing for real-time management of water resources W Kurtz, A Lapin, OS Schilling, Q Tang, EJ Schiller, T Braun, D Hunkeler, ... Environmental modelling & software 93, 418-435, 2017 | 50 | 2017 |
The influence of riverbed heterogeneity patterns on river-aquifer exchange fluxes under different connection regimes Q Tang, W Kurtz, OS Schilling, P Brunner, H Vereecken, HJH Franssen Journal of Hydrology 554, 383-396, 2017 | 42 | 2017 |
Characterisation of river–aquifer exchange fluxes: The role of spatial patterns of riverbed hydraulic conductivities Q Tang, W Kurtz, P Brunner, H Vereecken, HJH Franssen Journal of Hydrology 531, 111-123, 2015 | 36 | 2015 |
Simulating Flood‐Induced Riverbed Transience Using Unmanned Aerial Vehicles, Physically Based Hydrological Modeling, and the Ensemble Kalman Filter Q Tang, OS Schilling, W Kurtz, P Brunner, H Vereecken, ... Water Resources Research 54 (11), 9342-9363, 2018 | 21 | 2018 |
Bayesian networks precipitation model based on hidden Markov analysis and its application HR Wang, LT Ye, XY Xu, QL Feng, Y Jiang, Q Liu, Q Tang Science China Technological Sciences 53 (2), 539-547, 2010 | 14 | 2010 |
Improving the ocean and atmosphere in a coupled ocean–atmosphere model by assimilating satellite sea‐surface temperature and subsurface profile data Q Tang, L Mu, D Sidorenko, H Goessling, T Semmler, L Nerger Quarterly Journal of the Royal Meteorological Society 146 (733), 4014-4029, 2020 | 8 | 2020 |
Is it important to characterize complex patterns of riverbed hydraulic conductivities for assessing river-aquifer exchange fluxes? An evaluation with an integrated fully … Q Tang, W Kurtz, O Schilling, P Brunner, H Vereecken, ... EGUGA, EPSC2016-8550, 2016 | | 2016 |