Abstract
Abstract. Land surface modeling, in conjunction with numerical weather forecasting and satellite remote sensing, is playing an increasing role in global monitoring and prediction of extreme hydrologic events (i.e., floods and droughts). However, uncertainties in the meteorological forcings, model structure, and parameter identifiability limit the reliability of model predictions. This study focuses on the latter by assessing two potential weaknesses that emerge due to limitations in our global runoff observations: (1) the limits of identifying model parameters at coarser timescales than those at which the extreme events occur, and (2) the negative impacts of not properly accounting for model parameter equifinality in the predictions of extreme events. To address these challenges, petascale parallel computing is used to perform the first global-scale, 10 000 member ensemble-based evaluation of plausible model parameters using the VIC (Variable Infiltration Capacity) land surface model, aiming to characterize the impact of parameter identifiability on the uncertainty in flood and drought predictions. Additionally, VIC's global-scale parametric sensitivities are assessed at the annual, monthly, and daily timescales to determine whether coarse-timescale observations can properly constrain extreme events. Global and climate type results indicate that parameter uncertainty remains an important concern for predicting extreme events even after applying monthly and annual constraints to the ensemble, suggesting a need for improved prior distributions of the model parameters as well as improved observations. This study contributes a comprehensive evaluation of land surface modeling for global flood and drought monitoring and suggests paths forward to overcome the challenges posed by parameter uncertainty.
Highlights
Droughts and floods can have devastating consequences on ecosystems, food supply, and economies (Easterling et al, 2000)
For each land 1.0◦ grid cell (15 836) the Variable Infiltration Capacity model (VIC) (Variable Infiltration Capacity) land surface model is run between 1948 and 2010 at a 3-hour temporal resolution for 10 000 parameter sets obtained from a Latin hypercube sample
We suggest that flood and drought monitoring systems that aim to capture hydrologic extremes move towards model parameter ensemble frameworks to provide predictions and uncertainty estimates
Summary
Droughts and floods can have devastating consequences on ecosystems, food supply, and economies (Easterling et al, 2000). Providing real-time information and predictions to decision makers can be a valuable tool to mitigate their effects This is an especially challenging task over data-sparse regions, where unreliable monitoring networks and generally low institutional capacity limits the spread of timely information (Sheffield et al, 2013). The land surface model component of a monitoring system is useful to understand the impact of flood and drought on the energy, carbon, and hydrologic cycles. This is possible with the current generation of LSMs that include the main physical, biological, and chemical processes at the land surface (Niu et al, 2011). Sensitivity analysis of macroscale land surface models suggests that this is overly
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