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

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.

Mapping (dis)agreement in hydrologic projections

“…For each model, a representative set of parameters that capture the essential hydro-climatological features was iden-tified. A large range of parameters was sampled using a Sobol-based Latin hypercube sample: 17 parameters for VIC (Demaria et al, 2007;Chaney et al, 2015;Melsen et al, 2016;Mendoza et al, 2015b), 18 parameters for SAC (Newman et al, 2015;Lhomme, 1997), and 15 for HBV (Parajka et al, 2007;Uhlenbrook et al, 1999;Abebe et al, 2010). Physically realistic parameter boundaries were determined, based on the literature: see Tables C1-C3.…”

Towards simplification of hydrologic modeling: identification of dominant processes

“…", and the calibration problem: "I know the output I want, which parameters should I change and how much should I change them?") (Chaney et al, 2015;. While the user of a DPHM can do nothing about the complexity of the model's internal structure, the apparent complexity can be reduced by limiting the parameters and the affected output under consideration (as described by Jakeman and Hornberger, 1993;.…”

“…Identification and simulation of regional CONUS sub-watersheds are determined by the resolution of the available information and how the DPHM responds to geophysical (e.g., topography, vegetation and soils) and climatological variation. Specifically, we propose to identify the sensitive parameters and dominant hydrologic process(es), thereby reducing the amount of parameter input and number of output variables to consider (Chaney et al, 2015) and address the two aspects of complexity as described above.…”

Improvement of model evaluation by incorporating prediction and measurement uncertainty

“…Errors in these complex processes lead to uncertainty in the data (Chaney et al, 2015). Uncertainties in hydrology and NPS modelling are classified as either measurement uncertainty or prediction uncertainty Di Baldassarre and Montanari, 2009).…”