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

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, a...

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Bibliographic Details
Published in:Hydrology and earth system sciences Vol. 19; no. 7; pp. 3239 - 3251
Main Authors: Chaney, N. W., Herman, J. D., Reed, P. M., Wood, E. F.
Format: Journal Article
Language:English
Published: Katlenburg-Lindau Copernicus GmbH 24.07.2015
Copernicus Publications
Subjects:
Analysis
Climate
Climate models
Climatic extremes
Datasets
Drought
Drought monitoring
Environmental monitoring
Evaluation
Flood predictions
Floods
Flow velocity
Hydrologic cycle
Hydrologic drought
Hydrology
Infiltration
Infiltration capacity
Land surface models
Modelling
Numerical weather forecasting
Numerical weather prediction
Parameter estimation
Parameter identification
Parameter sensitivity
Parameter uncertainty
Parameters
Precipitation
Predictions
Reliability analysis
Remote monitoring
Remote sensing
Runoff
Satellite remote sensing
Satellites
Sensitivity analysis
Uncertainty
Vegetation
Weather forecasting
ISSN:1607-7938, 1027-5606, 1607-7938
Online Access:Get full text
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Summary: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.
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ISSN:1607-7938
1027-5606
1607-7938
DOI:10.5194/hess-19-3239-2015