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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Vydáno v:	Hydrology and earth system sciences Ročník 19; číslo 7; s. 3239 - 3251
Hlavní autoři:	Chaney, N. W., Herman, J. D., Reed, P. M., Wood, E. F.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Katlenburg-Lindau Copernicus GmbH 24.07.2015 Copernicus Publications
Témata:	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
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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.
AbstractList	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.
Audience	Academic
Author	Herman, J. D. Chaney, N. W. Reed, P. M. Wood, E. F.
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SubjectTerms	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
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Title	Flood and drought hydrologic monitoring: the role of model parameter uncertainty
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