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Land surface modeling, in conjunction with numerical weather forecasting andsatellite remote sensing, is playing an increasing role in global monitoringand prediction of extreme hydrologic events (i.e., floods and droughts).However, uncertainties in the meteorological forcings, model structure, andparameter identifiability limit the reliability of model predictions. Thisstudy focuses on the latter by assessing two potential weaknesses that emergedue to limitations in our global runoff observations: (1) the limits ofidentifying model parameters at coarser timescales than those at which theextreme events occur, and (2) the negative impacts of not properly accountingfor model parameter equifinality in the predictions of extreme events. Toaddress these challenges, petascale parallel computing is used to perform thefirst global-scale, 10 000 member ensemble-based evaluation of plausiblemodel parameters using the VIC (Variable Infiltration Capacity) land surfacemodel, aiming to characterize the impact of parameter identifiability on theuncertainty in flood and drought predictions. Additionally, VIC'sglobal-scale parametric sensitivities are assessed at the annual, monthly,and daily timescales to determine whether coarse-timescale observations canproperly constrain extreme events. Global and climate type results indicatethat parameter uncertainty remains an important concern for predictingextreme events even after applying monthly and annual constraints to theensemble, suggesting a need for improved prior distributions of the modelparameters as well as improved observations. This study contributes acomprehensive evaluation of land surface modeling for global flood anddrought monitoring and suggests paths forward to overcome the challengesposed by parameter uncertainty

Flood and drought hydrologic monitoring: the role of model parameter uncertainty